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#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6771712/bin/SIM-38-3896-s002.pdf
# Cumulative incidence using two cause-specific models
cumIncidence.CS <- function(model1,model2,time.max,subdiv,
data.val=data.frame(.NotUsed=NA),alpha=0.05){
time.pts <- seq(0,time.max,le=(subdiv+1))
CstMult <- time.max/(2*subdiv)
CstCI <- qnorm(1-alpha/2)
# Or if you want to take into account the size of the population
# CstCI <- qt(1-alpha/2,df=model£n.obs)
Pred1 <- predict(model1,time.pts,data.val, include.gradient=T)
Pred2 <- predict(model2,time.pts,data.val, include.gradient=T)
ISurv1 <- Pred1$results$hazard*Pred1$results$surv*Pred2$results$surv
CPr.1 <- cumsum(ISurv1+c(0,ISurv1[-subdiv]))*(time.max/(2*subdiv))
ISurv2 <- Pred2$results$hazard*Pred1$results$surv*Pred2$results$surv
CPr.2 <- cumsum(ISurv2+c(0,ISurv2[-subdiv]))*(time.max/(2*subdiv))
ISurvT <- (Pred1$results$hazard+Pred2$results$hazard)*Pred1$results$surv*
Pred2$results$surv
CPrT<- cumsum(ISurvT+c(0,ISurvT[-subdiv]))*(time.max/(2*subdiv))
SurvT <- 1-CPrT
# Confidence intervals
which.td1<- rownames(Pred1$vcov)[-c(1,which(rownames(Pred1$vcov)
%in% model1$names.ph))]
which.ntd1 <- c(rownames(Pred1$vcov)[-c(which(rownames(Pred1$vcov)
%in% model1$names.ph))][1],
rownames(Pred1$vcov)[which(rownames(Pred1$vcov) %in%
model1$names.ph)])
which.td2<- rownames(Pred2$vcov)[-c(1,which(rownames(Pred2$vcov) %in%
model2$names.ph))]
which.ntd2 <- c(rownames(Pred2$vcov)[-c(which(rownames(Pred2$vcov) %in%
model2$names.ph))][1],
rownames(Pred2$vcov)[which(rownames(Pred2$vcov) %in%
model2$names.ph)])
Vcov1 <- model1$vcov[c(which.ntd1,which.td1),c(which.ntd1,which.td1)]
Vcov2 <- model2$vcov[c(which.ntd2,which.td2),c(which.ntd2,which.td2)]
CovMat <- as.matrix(bdiag(Vcov1,Vcov2))
AGrad11 <- (Pred1$grad.loghaz + Pred1$grad.logcum*(log(Pred1$results$surv)))
[,c(which.ntd1,which.td1)]
AGrad12 <- (Pred2$grad.logcum*(log(Pred2$results$surv)))
[,c(which.ntd2,which.td2)]
AGrad21 <- (Pred1$grad.logcum*(log(Pred1$results$surv)))
[,c(which.ntd1,which.td1)]
AGrad22 <- (Pred2$grad.loghaz + Pred2$grad.logcum*(log(Pred2$results$surv)))
[,c(which.ntd2,which.td2)]
AGrad1 <- cbind(AGrad11,AGrad12)
AGrad2 <- cbind(AGrad21,AGrad22)
AGradT <- cbind(AGrad21,AGrad12)
Temp1 <- ISurv1*AGrad1
Denom1 <- (1-CPr.1)*log(1-CPr.1)
BGrad1 <- apply(Temp1,2,function(x) cumsum(x+c(0,x[-subdiv]))*
(time.max/(2*subdiv)))
TMatVar1 <- CovMat%*%t(BGrad1)
Var1 <- sapply(1:subdiv,function(i) BGrad1[i,]%*%TMatVar1[,i])
Var1b <- Var1/((1-CPr.1)*log(1-CPr.1))^2
Temp2 <- ISurv2*AGrad2
Denom2 <- (1-CPr.2)*log(1-CPr.2)^2
BGrad2 <- apply(Temp2,2,function(x) cumsum(x+c(0,x[-subdiv]))*
(time.max/(2*subdiv)))
TMatVar2 <- CovMat%*%t(BGrad2)
Var2 <- sapply(1:subdiv,function(i) BGrad2[i,]%*%TMatVar2[,i])
Var2b <- Var2/((1-CPr.2)*log(1-CPr.2))^2
BGradT <- AGradT/log(SurvT)
TMatVarT <- CovMat%*%t(BGradT)
VarT <- sapply(1:subdiv,function(i) BGradT[i,]%*%TMatVarT[,i])
Transf <- function(x,vx,m){
log(-log(x))+m*CstCI*sqrt(vx)
}
InvTransf <- function(x){
exp(-exp(x))
}
Cr1Lo <- 1-InvTransf(Transf(1-CPr.1,Var1b,-1))
Cr1Up <- 1-InvTransf(Transf(1-CPr.1,Var1b,1))
Cr2Lo <- 1-InvTransf(Transf(1-CPr.2,Var2b,-1))
Cr2Up <- 1-InvTransf(Transf(1-CPr.2,Var2b,1))
CrTLo <- 1-InvTransf(Transf(SurvT,VarT,-1))
CrTUp <- 1-InvTransf(Transf(SurvT,VarT,1))
return(list("time"= time.pts,
"frag"=subdiv,
"CPr.1"=CPr.1,"CPr.2"=CPr.2,"CovMat"=CovMat,
"BGrad1"=BGrad1,"BGrad2"=BGrad2,"CPr1Lo"= Cr1Lo,
"CPr1Up"=Cr1Up,"CPr2Lo"= Cr2Lo,"CPr2Up"=Cr2Up,
"Var1"=Var1, "Var2"=Var2,"Var1b"=Var1b,"Var2b"=Var2b))
}
#Functions for prediction
csprob<-function(data_df, modA, modB, time.max, frag){
results_list<- list()
for (y in 1:nrow(data_df)){
results_list[[y]] <- cumIncidence.CS(modA, modB,time.max,
subdiv=frag,
data.val=data_df[y,])
}
return(results_list)
}
BGrad_func <- function(BGrad.ls,frag,N, p_dim){
BGrad.3da <- array(unlist(BGrad.ls),dim = c(frag,p_dim,N))
BGrad.3d <- aperm(BGrad.3da,dim=c(3,1,2))
NBGrad.ls <- alply(BGrad.3d,3)
return(NBGrad.ls)
}
Var_func <- function (x,w,NBGrad, CovMat){
BGrad<- w%*%NBGrad[[x]]
TMatVar <- CovMat%*%t(BGrad)
res<- BGrad%*%TMatVar
return(res)
}
Transf <- function(x,vx,m){log(-log(x))+m*qnorm(0.975)*sqrt(vx)}
InvTransf <- function(x){exp(-exp(x))}
predict.prob <- function(csprobObj, pop){
CovMat <- csprobObj[[1]]$CovMat
p_dim <- dim(csprobObj[[1]]$CovMat)[1]
N <- length(csprobObj)
frag <- csprobObj[[1]]$frag
if (pop==FALSE){
BGrad1.ls <- lapply(1:N, function(x) csprobObj[[x]]$BGrad1)
BGrad2.ls <- lapply(1:N, function(x) csprobObj[[x]]$BGrad2)
CPr1.df <- sapply(1:N, function(x) csprobObj[[x]]$CPr.1)
CPr2.df <- sapply(1:N, function(x) csprobObj[[x]]$CPr.2)
CPr1Lo.df <- sapply(1:N, function(x) csprobObj[[x]]$CPr1Lo)
CPr1Up.df <- sapply(1:N, function(x) csprobObj[[x]]$CPr1Up)
CPr2Lo.df <- sapply(1:N, function(x) csprobObj[[x]]$CPr2Lo)
CPr2Up.df <- sapply(1:N, function(x) csprobObj[[x]]$CPr2Up)
return(list("time"=csprobObj[[1]]$time,"frag"=frag,
"BGrad1.ls"=BGrad1.ls, "BGrad2.ls"=BGrad2.ls,
"CPr1Lo.df"=CPr1Lo.df,"CPr1Up.df"=CPr1Up.df,
"CPr2Lo.df"=CPr2Lo.df,"CPr2Up.df"=CPr2Up.df,
"CPr1.df"=CPr1.df,"CPr2.df"=CPr2.df))
}
if (pop==TRUE){
w <- matrix(rep(1/N,N), nrow =1)
BGrad1.ls<- lapply(1:N, function(x) csprobObj[[x]]$BGrad1)
BGrad2.ls<- lapply(1:N, function(x) csprobObj[[x]]$BGrad2)
CPr1.df <- sapply(1:N, function(x) csprobObj[[x]]$CPr.1)
CPr2.df <- sapply(1:N, function(x) csprobObj[[x]]$CPr.2)
CPr.1 <- w%*%t(CPr1.df)
CPr.2 <- w%*%t(CPr2.df)
NBGrad1.ls <- BGrad_func(BGrad1.ls,frag,N,p_dim)
NBGrad2.ls <- BGrad_func(BGrad2.ls,frag,N,p_dim)
Var1 <- data.frame(sapply(1:frag, function(x)
Var_func(x,w,NBGrad1.ls, CovMat) ))
Var1b <- Var1/((1-CPr.1)*log(1-CPr.1))^2
CPr1Lo <- 1-InvTransf(Transf(1-CPr.1,Var1b,-1))
CPr1Up <- 1-InvTransf(Transf(1-CPr.1,Var1b,1))
Var2 <- data.frame(sapply(1:frag, function(x)
Var_func(x,w,NBGrad2.ls, CovMat) ))
Var2b <- Var2/((1-CPr.2)*log(1-CPr.2))^2
CPr2Lo <- 1-InvTransf(Transf(1-CPr.2,Var2b,-1))
CPr2Up <- 1-InvTransf(Transf(1-CPr.2,Var2b,1))
return(list("time"=csprobObj[[1]]$time[-1],"frag"=frag,
"BGrad1.ls"=BGrad1.ls, "BGrad2.ls"=BGrad2.ls,
"CPr.1"=CPr.1[1,],"CPr.2"=CPr.2[1,],"NBGrad1.ls"=NBGrad1.ls,
"NBGrad2.ls"=NBGrad2.ls,"Var1"=Var1,"Var2"=Var2,
"Var1b"=Var1b,"Var2b"=Var2b,
"CPr1Lo"=CPr1Lo[,1],"CPr1Up"=CPr1Up[,1],
"CPr2Lo"=CPr2Lo[,1],"CPr2Up"=CPr2Up[,1],
"CPr1.df"=CPr1.df,"CPr2.df"=CPr2.df,"CovMat"=CovMat))
}
}
# Calculate differences between populations
csprobdif<- function(predprob1, predprob2){
N <- ncol(predprob1$CPr1.df)
w<- matrix(rep(1/N,N), nrow =1)
frag <- predprob1$frag
CovMat <- predprob1$CovMat
NBGrad1.ls <- lapply(1:frag, function (x) predprob1$NBGrad1.ls[[x]]-
predprob2$NBGrad1.ls[[x]])
NBGrad2.ls <- lapply(1:frag, function (x) predprob1$NBGrad2.ls[[x]]-
predprob2$NBGrad2.ls[[x]])
CP1.dif_df <- predprob1$CPr1.df-predprob2$CPr1.df
CP2.dif_df <- predprob1$CPr2.df-predprob2$CPr2.df
CP1.dif <-apply(CP1.dif_df,1,mean)
CP2.dif<-apply(CP2.dif_df,1,mean)
ci_form <- function(x,var,m){
x+m*qnorm(0.975)*sqrt(var)
}
Var1 <-data.frame(sapply(1:frag, function(x)
Var_func(x,w,NBGrad1.ls,CovMat)))
Var2 <-data.frame(sapply(1:frag, function(x)
Var_func(x,w,NBGrad2.ls,CovMat)))
CPr1.difLo <- ci_form(CP1.dif,Var1,-1)[,1]
CPr1.difUp <- ci_form(CP1.dif,Var1,+1)[,1]
CPr2.difLo <- ci_form(CP2.dif,Var2,-1)[,1]
CPr2.difUp <- ci_form(CP2.dif,Var2,+1)[,1]
return(list("time"= predprob1$time,
"ProbDif1"=CP1.dif, "ProbDif2"=CP2.dif,
"ProbDif1Lo"=CPr1.difLo,"ProbDif1Up"=CPr1.difUp,
"ProbDif2Lo"=CPr2.difLo,"ProbDif2Up"=CPr2.difUp))
}Consolidate the database
We generated the database with the new requested variables. Must note that this database already filtererd minors at admission but people that did not reported a valid age (remember differences with Stata).
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invisible("84,936 x 254 # 2020-10-25")
invisible("84,944 x 277 # 2020-12-20")
prueba<-
CONS_C1_df_dup_SEP_2020 %>%
dplyr::group_by(hash_key) %>%
dplyr::mutate(menor_edad=dplyr::case_when(edad_al_ing<18~1,TRUE~0),menor_edad=sum(menor_edad,na.rm=T)) %>%
dplyr::ungroup() %>%
dplyr::filter(edad_al_ing>=18| is.na(edad_al_ing)) %>% #Sólo así llegamos a 109,642 casos, igual que en STATA
dplyr::group_by(hash_key) %>%
dplyr::mutate(dup2=row_number()) %>%
dplyr::mutate(duplicates_filtered2=n()) %>%
dplyr::mutate(max_cum_dias_trat_sin_na=max(cum_dias_trat_sin_na, na.rm=T)) %>%
dplyr::mutate(max_cum_diff_bet_treat=max(cum_diff_bet_treat, na.rm=T)) %>%
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#Formatear días de tratamiento y diferencia en días excliyendo a los casos a los que le borramos un tratamiento menor a 18.
#local vars `" "diff_bet_treat_" "dias_treat_imp_sin_na_" "cum_diff_bet_treat_" "cum_dias_trat_sin_na_" "mean_cum_dias_trat_sin_na_" "mean_cum_diff_bet_treat_" "'
dplyr::ungroup() %>%
dplyr::filter(dup2==1) %>% #84,936 x 254 # 2020-10-25,
dplyr::mutate(cum_dias_trat_sin_na_1= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_2,TRUE~cum_dias_trat_sin_na_1)) %>%
dplyr::mutate(cum_dias_trat_sin_na_2= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_3,TRUE~cum_dias_trat_sin_na_2)) %>%
dplyr::mutate(cum_dias_trat_sin_na_3= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_4,TRUE~cum_dias_trat_sin_na_3)) %>%
dplyr::mutate(cum_dias_trat_sin_na_4= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_5,TRUE~cum_dias_trat_sin_na_4)) %>%
dplyr::mutate(cum_dias_trat_sin_na_5= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_6,TRUE~cum_dias_trat_sin_na_5)) %>%
dplyr::mutate(cum_dias_trat_sin_na_6= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_7,TRUE~cum_dias_trat_sin_na_6)) %>%
dplyr::mutate(cum_dias_trat_sin_na_7= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_8,TRUE~cum_dias_trat_sin_na_7)) %>%
dplyr::mutate(cum_dias_trat_sin_na_8= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_9,TRUE~cum_dias_trat_sin_na_8)) %>%
dplyr::mutate(cum_dias_trat_sin_na_9= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_10,TRUE~cum_dias_trat_sin_na_9)) %>%
dplyr::mutate(cum_dias_trat_sin_na_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~cum_dias_trat_sin_na_10)) %>%
dplyr::mutate(cum_diff_bet_treat_1= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_2,TRUE~cum_diff_bet_treat_1)) %>%
dplyr::mutate(cum_diff_bet_treat_2= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_3,TRUE~cum_diff_bet_treat_2)) %>%
dplyr::mutate(cum_diff_bet_treat_3= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_4,TRUE~cum_diff_bet_treat_3)) %>%
dplyr::mutate(cum_diff_bet_treat_4= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_5,TRUE~cum_diff_bet_treat_4)) %>%
dplyr::mutate(cum_diff_bet_treat_5= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_6,TRUE~cum_diff_bet_treat_5)) %>%
dplyr::mutate(cum_diff_bet_treat_6= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_7,TRUE~cum_diff_bet_treat_6)) %>%
dplyr::mutate(cum_diff_bet_treat_7= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_8,TRUE~cum_diff_bet_treat_7)) %>%
dplyr::mutate(cum_diff_bet_treat_8= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_9,TRUE~cum_diff_bet_treat_8)) %>%
dplyr::mutate(cum_diff_bet_treat_9= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_10,TRUE~cum_diff_bet_treat_9)) %>%
dplyr::mutate(cum_diff_bet_treat_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~cum_diff_bet_treat_10)) %>%
dplyr::mutate(tipo_de_plan_2_1= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_2),TRUE~as.character(tipo_de_plan_2_1))) %>%
dplyr::mutate(tipo_de_plan_2_2= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_3),TRUE~as.character(tipo_de_plan_2_2))) %>%
dplyr::mutate(tipo_de_plan_2_3= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_4),TRUE~as.character(tipo_de_plan_2_3))) %>%
dplyr::mutate(tipo_de_plan_2_4= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_5),TRUE~as.character(tipo_de_plan_2_4))) %>%
dplyr::mutate(tipo_de_plan_2_5= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_6),TRUE~as.character(tipo_de_plan_2_5))) %>%
dplyr::mutate(tipo_de_plan_2_6= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_7),TRUE~as.character(tipo_de_plan_2_6))) %>%
dplyr::mutate(tipo_de_plan_2_7= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_8),TRUE~as.character(tipo_de_plan_2_7))) %>%
dplyr::mutate(tipo_de_plan_2_8= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_9),TRUE~as.character(tipo_de_plan_2_8))) %>%
dplyr::mutate(tipo_de_plan_2_9= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_10),TRUE~as.character(tipo_de_plan_2_9))) %>%
dplyr::mutate(tipo_de_plan_2_10= dplyr::case_when(menor_edad>0~NA_character_,TRUE~as.character(tipo_de_plan_2_10))) %>%
dplyr::mutate(motivodeegreso_mod_imp_1= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_2),TRUE~as.character(motivodeegreso_mod_imp_1))) %>%
dplyr::mutate(motivodeegreso_mod_imp_2= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_3),TRUE~as.character(motivodeegreso_mod_imp_2))) %>%
dplyr::mutate(motivodeegreso_mod_imp_3= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_4),TRUE~as.character(motivodeegreso_mod_imp_3))) %>%
dplyr::mutate(motivodeegreso_mod_imp_4= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_5),TRUE~as.character(motivodeegreso_mod_imp_4))) %>%
dplyr::mutate(motivodeegreso_mod_imp_5= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_6),TRUE~as.character(motivodeegreso_mod_imp_5))) %>%
dplyr::mutate(motivodeegreso_mod_imp_6= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_7),TRUE~as.character(motivodeegreso_mod_imp_6))) %>%
dplyr::mutate(motivodeegreso_mod_imp_7= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_8),TRUE~as.character(motivodeegreso_mod_imp_7))) %>%
dplyr::mutate(motivodeegreso_mod_imp_8= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_9),TRUE~as.character(motivodeegreso_mod_imp_8))) %>%
dplyr::mutate(motivodeegreso_mod_imp_9= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_10),TRUE~as.character(motivodeegreso_mod_imp_9))) %>%
dplyr::mutate(motivodeegreso_mod_imp_10= dplyr::case_when(menor_edad>0~NA_character_,TRUE~as.character(motivodeegreso_mod_imp_10))) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_1= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_2,TRUE~mean_cum_dias_trat_sin_na_1)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_2= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_3,TRUE~mean_cum_dias_trat_sin_na_2)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_3= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_4,TRUE~mean_cum_dias_trat_sin_na_3)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_4= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_5,TRUE~mean_cum_dias_trat_sin_na_4)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_5= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_6,TRUE~mean_cum_dias_trat_sin_na_5)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_6= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_7,TRUE~mean_cum_dias_trat_sin_na_6)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_7= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_8,TRUE~mean_cum_dias_trat_sin_na_7)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_8= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_9,TRUE~mean_cum_dias_trat_sin_na_8)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_9= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_10,TRUE~mean_cum_dias_trat_sin_na_9)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~mean_cum_dias_trat_sin_na_10)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_1= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_2,TRUE~mean_cum_diff_bet_treat_1)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_2= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_3,TRUE~mean_cum_diff_bet_treat_2)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_3= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_4,TRUE~mean_cum_diff_bet_treat_3)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_4= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_5,TRUE~mean_cum_diff_bet_treat_4)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_5= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_6,TRUE~mean_cum_diff_bet_treat_5)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_6= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_7,TRUE~mean_cum_diff_bet_treat_6)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_7= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_8,TRUE~mean_cum_diff_bet_treat_7)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_8= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_9,TRUE~mean_cum_diff_bet_treat_8)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_9= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_10,TRUE~mean_cum_diff_bet_treat_9)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~mean_cum_diff_bet_treat_10)) %>%
dplyr::mutate(diff_bet_treat_1= dplyr::case_when(menor_edad>0~diff_bet_treat_2,TRUE~diff_bet_treat_1)) %>%
dplyr::mutate(diff_bet_treat_2= dplyr::case_when(menor_edad>0~diff_bet_treat_3,TRUE~diff_bet_treat_2)) %>%
dplyr::mutate(diff_bet_treat_3= dplyr::case_when(menor_edad>0~diff_bet_treat_4,TRUE~diff_bet_treat_3)) %>%
dplyr::mutate(diff_bet_treat_4= dplyr::case_when(menor_edad>0~diff_bet_treat_5,TRUE~diff_bet_treat_4)) %>%
dplyr::mutate(diff_bet_treat_5= dplyr::case_when(menor_edad>0~diff_bet_treat_6,TRUE~diff_bet_treat_5)) %>%
dplyr::mutate(diff_bet_treat_6= dplyr::case_when(menor_edad>0~diff_bet_treat_7,TRUE~diff_bet_treat_6)) %>%
dplyr::mutate(diff_bet_treat_7= dplyr::case_when(menor_edad>0~diff_bet_treat_8,TRUE~diff_bet_treat_7)) %>%
dplyr::mutate(diff_bet_treat_8= dplyr::case_when(menor_edad>0~diff_bet_treat_9,TRUE~diff_bet_treat_8)) %>%
dplyr::mutate(diff_bet_treat_9= dplyr::case_when(menor_edad>0~diff_bet_treat_10,TRUE~diff_bet_treat_9)) %>%
dplyr::mutate(diff_bet_treat_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~diff_bet_treat_10)) %>%
dplyr::mutate(dias_treat_imp_sin_na_1= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_2,TRUE~dias_treat_imp_sin_na_1)) %>%
dplyr::mutate(dias_treat_imp_sin_na_2= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_3,TRUE~dias_treat_imp_sin_na_2)) %>%
dplyr::mutate(dias_treat_imp_sin_na_3= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_4,TRUE~dias_treat_imp_sin_na_3)) %>%
dplyr::mutate(dias_treat_imp_sin_na_4= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_5,TRUE~dias_treat_imp_sin_na_4)) %>%
dplyr::mutate(dias_treat_imp_sin_na_5= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_6,TRUE~dias_treat_imp_sin_na_5)) %>%
dplyr::mutate(dias_treat_imp_sin_na_6= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_7,TRUE~dias_treat_imp_sin_na_6)) %>%
dplyr::mutate(dias_treat_imp_sin_na_7= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_8,TRUE~dias_treat_imp_sin_na_7)) %>%
dplyr::mutate(dias_treat_imp_sin_na_8= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_9,TRUE~dias_treat_imp_sin_na_8)) %>%
dplyr::mutate(dias_treat_imp_sin_na_9= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_10,TRUE~dias_treat_imp_sin_na_9)) %>%
dplyr::mutate(dias_treat_imp_sin_na_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~dias_treat_imp_sin_na_10)) %>%
# diff y dias treat
dplyr::mutate(dias_treat_imp_sin_na_four = rowMeans(dplyr::select(., dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10), na.rm=T)) %>% #,na.rm=F
dplyr::mutate(diff_bet_treat_four = rowMeans(dplyr::select(., diff_bet_treat_4, diff_bet_treat_5, diff_bet_treat_6, diff_bet_treat_7, diff_bet_treat_8, diff_bet_treat_9, diff_bet_treat_10), na.rm=T)) %>% #,na.rm=F
# cum
dplyr::mutate(cum_dias_trat_sin_na_four = rowMeans(dplyr::select(., cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10), na.rm=T)) %>% #,na.rm=F
dplyr::mutate(cum_diff_bet_treat_four = rowMeans(dplyr::select(., cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10), na.rm=T)) %>% #,na.rm=F
# mean cum
dplyr::mutate(mean_cum_dias_trat_sin_na_four = rowMeans(dplyr::select(., mean_cum_dias_trat_sin_na_4, mean_cum_dias_trat_sin_na_5, mean_cum_dias_trat_sin_na_6, mean_cum_dias_trat_sin_na_7, mean_cum_dias_trat_sin_na_8, mean_cum_dias_trat_sin_na_9, mean_cum_dias_trat_sin_na_10), na.rm=T)) %>% #,na.rm=F
dplyr::mutate(mean_cum_diff_bet_treat_four = rowMeans(dplyr::select(., mean_cum_diff_bet_treat_4, mean_cum_diff_bet_treat_5, mean_cum_diff_bet_treat_6, mean_cum_diff_bet_treat_7, mean_cum_diff_bet_treat_8, mean_cum_diff_bet_treat_9, mean_cum_diff_bet_treat_10), na.rm=T)) %>% #,na.rm=F
# tipo de plana
dplyr::mutate(tipo_de_plan_2_mod=dplyr::case_when(grepl("PAB",tipo_de_plan_2)~"PAB",
grepl("PAI",tipo_de_plan_2)~"PAI",
grepl("PR",tipo_de_plan_2)~"PR",
TRUE~NA_character_)) %>%
dplyr::mutate(tipo_de_plan_2_mod=factor(tipo_de_plan_2_mod)) %>%
dplyr::mutate(estatus_ocupacional= dplyr::case_when(!is.na(cat_ocupacional)&!is.na(estatus_ocupacional)~"Empleado",
TRUE~as.character(estatus_ocupacional)))%>%
dplyr::mutate(estatus_ocupacional= as.factor(estatus_ocupacional))%>%
dplyr::mutate(cnt_mod_cie_10_dg_cons_sus_or= dplyr::case_when(as.character(dg_trs_cons_sus_or)=="Drug dependence"~dg_total_cie_10+1,
TRUE~dg_total_cie_10))%>%
dplyr::mutate(freq_cons_sus_prin= dplyr::case_when(as.character(freq_cons_sus_prin)=="Did not use"~"Less than 1 day a week",
TRUE~as.character(freq_cons_sus_prin)))%>%
dplyr::mutate(freq_cons_sus_prin= as.factor(freq_cons_sus_prin)) %>%
#edad más 65
dplyr::mutate(mas_65= dplyr::case_when(edad_al_ing>65~1,
TRUE~0))%>%
dplyr::mutate(mas_65= as.factor(mas_65)) %>%
dplyr::mutate(comorbidity_icd_10=dplyr::case_when(dg_total_cie_10>=2~ "Two or more",
dg_total_cie_10==1~ "One",
as.character(dg_cie_10_rec)=="Diagnosis unknown (under study)"~"Diagnosis unknown (under study)",
as.character(dg_cie_10_rec)=="Without psychiatric comorbidity"~"Without psychiatric comorbidity")) %>%
dplyr::mutate(comorbidity_icd_10=as.factor(comorbidity_icd_10)) %>%
# dplyr::select(-menor_edad) %>%
dplyr::mutate(no_group=1) %>%
dplyr::mutate(had_readm=dplyr::case_when(duplicates_filtered2>1~1,
TRUE~0)) %>%
dplyr::mutate(n_treats=factor(dplyr::case_when(duplicates_filtered2>3~"04 or more",
duplicates_filtered2==3~"03",
duplicates_filtered2==2~"02",
duplicates_filtered2==1~"01"))) Updated in 2021 June & August
We only left users in treatments in General Population (PG) and missing values and discarded under 18 years (n= 84,944). Additionally, we recategorized the variable ‘Living with’ (con_quien_vive).
Show code
#janitor::tabyl(CONS_C1_df_dup_SEP_2020$otros_probl_at_sm_or)
#round(prop.table(table(CONS_C1_df_dup_SEP_2020$otros_probl_at_sm_or)),3)
#With With Other Alone With With
#relatives couple and children children only couple only
#38105 21806 7275 8026 3191 6540
library(readr)
prueba2<-
prueba %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#AUG 2021:
dplyr::mutate(con_quien_vive_joel=dplyr::case_when(
grepl("Solo$",con_quien_vive, ignore.case=T)~"Alone",
grepl("Con abuelos",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("Con hermanos",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("Con la madre \\(sola\\)",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("Con otro pariente",con_quien_vive, ignore.case=T)~"Others",
grepl("con hijos y padres o familia",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("con la pareja y padres o familia de origen",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("con padres o familia de origen",con_quien_vive, ignore.case=T)~"Family of origin",
#2021-10-01
grepl("Únicamente con hijos",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("Únicamente con pareja",con_quien_vive, ignore.case=T)~"With couple/children",
#2021-10-01
grepl("Con la Pareja, Hijos y Padres o Familia de Origen",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("Hijos y Padres o Familia de Origen",con_quien_vive, ignore.case=T)~"Family of origin",
#2021-10-01
grepl("Únicamente con la pareja e hijos",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("Con amigos",con_quien_vive, ignore.case=T)~"Others",
grepl("Con otro NO pariente",con_quien_vive, ignore.case=T)~"Others",
grepl("*Otros$",con_quien_vive, ignore.case=T)~"Others")) %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#janitor::tabyl(con_quien_vive, con_quien_vive_rec)
dplyr::filter(!grepl("M-",tipo_de_plan_2)) %>%
#janitor::tabyl(embarazo)
#No Si <NA>
#76081 496 89
#prueba2 %>% janitor::tabyl(embarazo,tiene_menores_de_edad_a_cargo)
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::mutate(numero_de_hijos_mod_joel=dplyr::case_when(
grepl("Si$",embarazo, ignore.case=T)~as.integer(numero_de_hijos_mod+1),
T~as.integer(numero_de_hijos_mod)))%>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::filter(edad_al_ing_grupos=="18-29") %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::mutate(dom_violence=factor(dplyr::case_when(
grepl("Violencia Intrafamiliar$",otros_probl_at_sm_or, ignore.case=T)~1,
is.na(otros_probl_at_sm_or)~NA_real_,
T~0),levels=c(0,1),labels=c("No domestic violence","Domestic violence"))) %>%
dplyr::mutate(sex_abuse=factor(dplyr::case_when(
grepl("Abuso Sexual",otros_probl_at_sm_or, ignore.case=T)~1,
is.na(otros_probl_at_sm_or)~NA_real_,
T~0),levels=c(0,1),labels=c("No sexual abuse","Sexual abuse"))) %>%
dplyr::filter(!grepl("Others",con_quien_vive_joel)) %>%
#5 de agosto, paso de 26,236 a 23,979, descartando 2,257 casos que tienen "otros".
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#TENENCIA VIVIENDA
#Allegado, Arrienda, Cedida, Ocupación Irregular, Otros, Paga dividendo, Propia
#vive transitoriamente en casa ajena
dplyr::mutate(tenencia_de_la_vivienda_mod=
factor(dplyr::case_when(tenencia_de_la_vivienda_mod=="Allegado"~"Stays temporarily with a relative",
tenencia_de_la_vivienda_mod=="Arrienda"~"Renting",
tenencia_de_la_vivienda_mod=="Cedida"~"Owner/Transferred dwellings/Pays Dividends",
tenencia_de_la_vivienda_mod=="Ocupación Irregular"~"Illegal Settlement",
tenencia_de_la_vivienda_mod=="Otros"~"Others",
tenencia_de_la_vivienda_mod=="Paga dividendo"~"Owner/Transferred dwellings/Pays Dividends",
tenencia_de_la_vivienda_mod=="Propia"~"Owner/Transferred dwellings/Pays Dividends",
T~NA_character_))) %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#DAR ESTRUCTURA ORDINAL#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::mutate(escolaridad_rec=parse_factor(as.character(escolaridad_rec),levels=c('3-Completed primary school or less', '2-Completed high school or less', '1-More than high school'), ordered =T,trim_ws=T,include_na =F, locale=locale(encoding = "Latin1"))) %>%
dplyr::mutate(freq_cons_sus_prin=parse_factor(as.character(freq_cons_sus_prin),levels=c('Less than 1 day a week','2 to 3 days a week','4 to 6 days a week','1 day a week or more','Daily'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
dplyr::mutate(compromiso_biopsicosocial=parse_factor(as.character(compromiso_biopsicosocial),levels=c('1-Mild', '2-Moderate','3-Severe'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
dplyr::mutate(comorbidity_icd_10=parse_factor(as.character(comorbidity_icd_10),levels=c('Without psychiatric comorbidity', 'Diagnosis unknown (under study)','One','Two or more'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
dplyr::mutate(cnt_mod_cie_10_or=parse_factor(as.character(cnt_mod_cie_10_or),levels=c('0', '1','2','3'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#dplyr::mutate(edad_al_ing_grupos=if_else(edad_al_ing_grupos=='18-29','<18-29',as.character(edad_al_ing_grupos),NA_character_)) %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#FRECUENCIA CONSUMO: Regroup 1 día a la semana y No usó en el último mes
dplyr::mutate(freq_cons_sus_prin=if_else(freq_cons_sus_prin=='Did not use','Less than 1 day a week',as.character(freq_cons_sus_prin),NA_character_))
#"escolaridad_rec", "freq_cons_sus_prin", "num_otras_sus_mod", "numero_de_hijos_mod_joel", "cnt_mod_cie_10_or", "comorbidity_icd_10", "compromiso_biopsicosocial"
#numero_de_hijos_mod #Número de Hijos (Valor Max.)/Number of Children (Max. Value)
#hijos_trat_res #Tiene Hijos en Ingreso a Tratamiento Residencial del Último Registro/Have Children in Residential Treatment of the Last Entry
#prueba2 %>% janitor::tabyl(numero_de_hijos_mod,embarazo)
#prueba2 %>% janitor::tabyl(numero_de_hijos_mod,hijos_trat_res) #me parece que está bien, no hay contradicciones
#prueba2 %>% janitor::tabyl(con_quien_vive,con_quien_vive_joel)
#prueba2 %>% janitor::tabyl(con_quien_vive,con_quien_vive_joel) %>% copiar_nombres()
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:We ended having 23,979 users.
In the data base for 2021, only includes general population or mixed-gender programs at baseline. The variable whether the respondent had children at admission to a residential treatment had many missing values (16%). In the cases that is feasible to have children, it shows 3% of missing values.
Must note that there are patients that may respond that they have children in treatment and respond that they have more than a child. But there can be people that did not report children and having children in residential treatments.
- ‘Starting substance’ (
sus_ini_mod_mvv)= Most vulnerable value. - ‘Number of children’(
numero_de_hijos_mod_joel)= If a user was pregnant, we added one children into the count of the number of children, because of the sparsity of pregnancy status (5.22%). - ‘Primary or main substance’(
sus_principal_mod)= We used the primary substance at admission of the largest treatment for continuous treatments. Every substance was chosen from the largest treatment, excepting cases where the value was not available in the largest treatment. - ‘Tenure status of households’ (
tenencia_de_la_vivienda_mod)= Three categories were collapsed into one single condition: Owner (28.66%), with Transferred dwellings (3.76%) and Pays Dividend (1.79%). - ‘Educational Attainment’ (
escolaridad_rec)= We selected the most vulnerable category among continuous treatments for the same user. - ‘Consumption frequency of primary or main substance’(
freq_cons_sus_prin)= Among the categories of the Frequency of Drug Consumption, we grouped Less than 1 day a week (3.16%) with Did not use in the last 30 days (1.80%)
Total
Show code
#browse hash_key duplicates_filtered2 cum_diff_bet_treat dup2_real diff_bet_treat if hash_key=="0737aacbb7efdd418f7a37ce3386ce5e"|hash_key=="07668f2d3e4f6beb7975e43ee96eac80"
#diff_bet_treat_five+ cum_dias_trat_sin_na_five+ cum_diff_bet_treat_five+
# mean_cum_dias_trat_sin_na_five+ mean_cum_diff_bet_treat_five,
attr(prueba$n_treats,"label") <- "No. of treatments with 18+ at admission between 2010 and 2019"
attr(prueba$comorbidity_icd_10,"label") <- "Comorbidity ICD-10 (with amount of different diagnosis)"
attr(prueba$dias_treat_imp_sin_na_four,"label") <- "Days of Treatment (Fourth or those that follow)"
attr(prueba$diff_bet_treat_four,"label") <- "Days of Difference Between Treatments (Fifth treatment or those that folow)"
attr(prueba$cum_dias_trat_sin_na_four,"label") <- "Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba$cum_diff_bet_treat_four,"label") <- "Cumulative Difference Between Treatments (Fifth or those that follow)" #hash_key
attr(prueba$mean_cum_dias_trat_sin_na_four,"label") <- "Average Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba$mean_cum_diff_bet_treat_four,"label") <- "Average Cumulative Difference Between Treatments (Fifth or those that follow)"
attr(prueba$cnt_mod_cie_10_dg_cons_sus_or,"label") <- "Total count of Psychiatric & Drug dependence Diagnostics"
attr(prueba$max_cum_diff_bet_treat,"label") <- "Max. Cumulative Difference Between Treatments"
attr(prueba$max_cum_dias_trat_sin_na,"label") <- "Max. Cumulative Days of Treatment"
attr(prueba$tipo_de_plan_2_mod,"label") <- "Type of Plan (Independently of the Program)"
attr(prueba$condicion_ocupacional_corr,"label") <- 'Occupational Status Corrected(f)'
attr(prueba$cat_ocupacional_corr,"label") <- 'Occupational Category Corrected(f)'
attr(prueba$compromiso_biopsicosocial,"label") <- 'Biopsychosocial Compromise'
attr(prueba$tenencia_de_la_vivienda_mod,"label") <- 'Tenure status of households'
#attr(prueba$,"label")<-"Starting Substance"
attr(prueba$sus_principal_mod,"label")<-"Starting Substance"
attr(prueba$cum_dias_trat_sin_na_1,"label") <- "Cum. Days of Treatment (1st Treatment)"
attr(prueba$cum_dias_trat_sin_na_2,"label") <- "Cum. Days of Treatment (2nd Treatment)"
attr(prueba$cum_dias_trat_sin_na_3,"label") <- "Cum. Days of Treatment (3rd Treatment)"
attr(prueba$cum_dias_trat_sin_na_4,"label") <- "Cum. Days of Treatment (4th Treatment)"
attr(prueba$cum_dias_trat_sin_na_5,"label") <- "Cum. Days of Treatment (5th Treatment)"
attr(prueba$cum_dias_trat_sin_na_6,"label") <- "Cum. Days of Treatment (6th Treatment)"
attr(prueba$cum_dias_trat_sin_na_7,"label") <- "Cum. Days of Treatment (7th Treatment)"
attr(prueba$cum_dias_trat_sin_na_8,"label") <- "Cum. Days of Treatment (8th Treatment)"
attr(prueba$cum_dias_trat_sin_na_9,"label") <- "Cum. Days of Treatment (9th Treatment)"
attr(prueba$cum_dias_trat_sin_na_10,"label") <-"Cum. Days of Treatment (10th Treatment)"
attr(prueba$cum_diff_bet_treat_1,"label") <- "Cum. Diff Between Treatments (1st Treatment)"
attr(prueba$cum_diff_bet_treat_2,"label") <- "Cum. Diff Between Treatments (2nd Treatment)"
attr(prueba$cum_diff_bet_treat_3,"label") <- "Cum. Diff Between Treatments (3rd Treatment)"
attr(prueba$cum_diff_bet_treat_4,"label") <- "Cum. Diff Between Treatments (4th Treatment)"
attr(prueba$cum_diff_bet_treat_5,"label") <- "Cum. Diff Between Treatments (5th Treatment)"
attr(prueba$cum_diff_bet_treat_6,"label") <- "Cum. Diff Between Treatments (6th Treatment)"
attr(prueba$cum_diff_bet_treat_7,"label") <- "Cum. Diff Between Treatments (7th Treatment)"
attr(prueba$cum_diff_bet_treat_8,"label") <- "Cum. Diff Between Treatments (8th Treatment)"
attr(prueba$cum_diff_bet_treat_9,"label") <- "Cum. Diff Between Treatments (9th Treatment)"
attr(prueba$cum_diff_bet_treat_10,"label") <-"Cum. Diff Between Treatments (10th Treatment)"
attr(prueba2$n_treats,"label") <- "No. of treatments with 18+ at admission between 2010 and 2019"
attr(prueba2$comorbidity_icd_10,"label") <- "Comorbidity ICD-10 (with amount of different diagnosis)"
attr(prueba2$dias_treat_imp_sin_na_four,"label") <- "Days of Treatment (Fourth or those that follow)"
attr(prueba2$diff_bet_treat_four,"label") <- "Days of Difference Between Treatments (Fifth treatment or those that folow)"
attr(prueba2$cum_dias_trat_sin_na_four,"label") <- "Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba2$cum_diff_bet_treat_four,"label") <- "Cumulative Difference Between Treatments (Fifth or those that follow)" #hash_key
attr(prueba2$mean_cum_dias_trat_sin_na_four,"label") <- "Average Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba2$mean_cum_diff_bet_treat_four,"label") <- "Average Cumulative Difference Between Treatments (Fifth or those that follow)"
attr(prueba2$cnt_mod_cie_10_dg_cons_sus_or,"label") <- "Total count of Psychiatric & Drug dependence Diagnostics"
attr(prueba2$max_cum_diff_bet_treat,"label") <- "Max. Cumulative Difference Between Treatments"
attr(prueba2$max_cum_dias_trat_sin_na,"label") <- "Max. Cumulative Days of Treatment"
attr(prueba2$tipo_de_plan_2_mod,"label") <- "Type of Plan (Independently of the Program)"
attr(prueba2$condicion_ocupacional_corr,"label") <- 'Occupational Status Corrected(f)'
attr(prueba2$cat_ocupacional_corr,"label") <- 'Occupational Category Corrected(f)'
attr(prueba2$con_quien_vive_joel,"label") <- 'Whom you live with(cohabitation status) (Recoded) (f)'
attr(prueba2$compromiso_biopsicosocial,"label") <- 'Biopsychosocial Compromise'
attr(prueba2$tenencia_de_la_vivienda_mod,"label") <- 'Tenure status of households'
attr(prueba2$sus_ini_mod_mvv,"label")<-"Starting Substance"
attr(prueba2$cum_dias_trat_sin_na_1,"label") <- "Cum. Days of Treatment (1st Treatment)"
attr(prueba2$cum_dias_trat_sin_na_2,"label") <- "Cum. Days of Treatment (2nd Treatment)"
attr(prueba2$cum_dias_trat_sin_na_3,"label") <- "Cum. Days of Treatment (3rd Treatment)"
attr(prueba2$cum_dias_trat_sin_na_4,"label") <- "Cum. Days of Treatment (4th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_5,"label") <- "Cum. Days of Treatment (5th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_6,"label") <- "Cum. Days of Treatment (6th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_7,"label") <- "Cum. Days of Treatment (7th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_8,"label") <- "Cum. Days of Treatment (8th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_9,"label") <- "Cum. Days of Treatment (9th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_10,"label") <-"Cum. Days of Treatment (10th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_1,"label") <- "Days of Treatment (1st Treatment)"
attr(prueba2$dias_treat_imp_sin_na_2,"label") <- "Days of Treatment (2nd Treatment)"
attr(prueba2$dias_treat_imp_sin_na_3,"label") <- "Days of Treatment (3rd Treatment)"
attr(prueba2$dias_treat_imp_sin_na_4,"label") <- "Days of Treatment (4th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_5,"label") <- "Days of Treatment (5th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_6,"label") <- "Days of Treatment (6th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_7,"label") <- "Days of Treatment (7th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_8,"label") <- "Days of Treatment (8th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_9,"label") <- "Days of Treatment (9th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_10,"label") <-"Days of Treatment (10th Treatment)"
attr(prueba2$cum_diff_bet_treat_1,"label") <- "Cum. Diff Between Treatments (1st Treatment)"
attr(prueba2$cum_diff_bet_treat_2,"label") <- "Cum. Diff Between Treatments (2nd Treatment)"
attr(prueba2$cum_diff_bet_treat_3,"label") <- "Cum. Diff Between Treatments (3rd Treatment)"
attr(prueba2$cum_diff_bet_treat_4,"label") <- "Cum. Diff Between Treatments (4th Treatment)"
attr(prueba2$cum_diff_bet_treat_5,"label") <- "Cum. Diff Between Treatments (5th Treatment)"
attr(prueba2$cum_diff_bet_treat_6,"label") <- "Cum. Diff Between Treatments (6th Treatment)"
attr(prueba2$cum_diff_bet_treat_7,"label") <- "Cum. Diff Between Treatments (7th Treatment)"
attr(prueba2$cum_diff_bet_treat_8,"label") <- "Cum. Diff Between Treatments (8th Treatment)"
attr(prueba2$cum_diff_bet_treat_9,"label") <- "Cum. Diff Between Treatments (9th Treatment)"
attr(prueba2$cum_diff_bet_treat_10,"label") <-"Cum. Diff Between Treatments (10th Treatment)"
attr(prueba2$numero_de_hijos_mod_joel,"label") <-"Number of Children (Max. Value), adding 1 if pregnant at admission"
attr(prueba2$sex_abuse,"label") <-"Sexual abuse"
attr(prueba2$dom_violence,"label") <-"Domestic violence"
attr(prueba2$freq_cons_sus_prin,"label") <-"Frequency of drug use in the primary substance"
#n_treats mean_cum_diff_bet_treat_four mean_cum_diff_bet_treat_four cum_diff_bet_treat_four cum_dias_trat_sin_na_four diff_bet_treat_four dias_treat_imp_sin_na_four cnt_mod_cie_10_dg_cons_sus_or max_cum_diff_bet_treat max_cum_dias_trat_sin_na cum_diff_bet_treat_10 cum_dias_trat_sin_na_10 tipo_de_plan_2_mod
library(compareGroups)
table3 <- compareGroups::compareGroups(no_group ~ sexo_2+ escolaridad_rec+ estado_conyugal_2+ compromiso_biopsicosocial+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+ cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+ dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(sexo_2=3,
escolaridad_rec=3,
estado_conyugal_2=3,
compromiso_biopsicosocial=2,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered= 3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 2,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = prueba2,
include.miss = T,
var.equal=T
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table3)
#p.adjust(pvals, method = "BH")
restab3 <- createTable(table3,show.p.overall = F)
compareGroups::export2md(restab3, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Total"),
format="html",caption= "Summary descriptives table")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")| Variables | Total |
|---|---|
| N=23979 | |
| Sexo Usuario/Sex of User: | |
| Men | 19850 (82.8%) |
| Women | 4129 (17.2%) |
| escolaridad_rec: | |
| 3-Completed primary school or less | 5634 (23.5%) |
| 2-Completed high school or less | 14518 (60.5%) |
| 1-More than high school | 3751 (15.6%) |
| ‘Missing’ | 76 (0.32%) |
| Estado Conyugal/Marital Status: | |
| Married/Shared living arrangements | 5127 (21.4%) |
| Separated/Divorced | 501 (2.09%) |
| Single | 18294 (76.3%) |
| Widower | 30 (0.13%) |
| ‘Missing’ | 27 (0.11%) |
| Biopsychosocial Compromise: | |
| 1-Mild | 2240 (9.34%) |
| 2-Moderate | 13949 (58.2%) |
| 3-Severe | 7352 (30.7%) |
| ‘Missing’ | 438 (1.83%) |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 15.0 [13.0;16.0] |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 25.4 [22.6;27.7] |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | |
| Alcohol | 10341 (43.1%) |
| Cocaine hydrochloride | 934 (3.90%) |
| Cocaine paste | 1054 (4.40%) |
| Marijuana | 9165 (38.2%) |
| Other | 411 (1.71%) |
| ‘Missing’ | 2074 (8.65%) |
| Starting Substance: | |
| Alcohol | 10182 (42.5%) |
| Cocaine hydrochloride | 970 (4.05%) |
| Marijuana | 9255 (38.6%) |
| Other | 398 (1.66%) |
| Cocaine paste | 1100 (4.59%) |
| ‘Missing’ | 2074 (8.65%) |
| Frequency of drug use in the primary substance: | |
| 1 day a week or more | 1616 (6.74%) |
| 2 to 3 days a week | 7059 (29.4%) |
| 4 to 6 days a week | 4105 (17.1%) |
| Daily | 9911 (41.3%) |
| Less than 1 day a week | 1166 (4.86%) |
| ‘Missing’ | 122 (0.51%) |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | |
| Smoked or Pulmonary Aspiration | 13902 (58.0%) |
| Intranasal (powder aspiration) | 5575 (23.2%) |
| Injected Intravenously or Intramuscularly | 16 (0.07%) |
| Oral (drunk or eaten) | 4470 (18.6%) |
| Other | 12 (0.05%) |
| ‘Missing’ | 4 (0.02%) |
| Whom you live with(cohabitation status) (Recoded) (f): | |
| Alone | 1335 (5.57%) |
| Family of origin | 15455 (64.5%) |
| With couple/children | 7189 (30.0%) |
| Number of Children (Max. Value), adding 1 if pregnant at admission | 1.00 [0.00;1.00] |
| Occupational Status Corrected(f): | |
| Employed | 10423 (43.5%) |
| Inactive | 2019 (8.42%) |
| Looking for a job for the first time | 97 (0.40%) |
| No activity | 1333 (5.56%) |
| Not seeking for work | 216 (0.90%) |
| Unemployed | 9891 (41.2%) |
| Occupational Category Corrected(f): | |
| Employer | 322 (1.34%) |
| Other | 222 (0.93%) |
| Salaried | 7039 (29.4%) |
| Self-employed | 1934 (8.07%) |
| Unpaid family labour | 65 (0.27%) |
| Volunteer worker | 39 (0.16%) |
| ‘Missing’ | 14358 (59.9%) |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | |
| Mayor o igual a 90 días | 17383 (72.5%) |
| Menos de 90 días | 6596 (27.5%) |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | |
| Without psychiatric comorbidity | 9259 (38.6%) |
| Diagnosis unknown (under study) | 5198 (21.7%) |
| With psychiatric comorbidity | 9522 (39.7%) |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 147 [84.0;254] |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] |
| Type of Plan (Independently of the Program): | |
| PAB | 9277 (38.7%) |
| PAI | 11390 (47.5%) |
| PR | 3286 (13.7%) |
| ‘Missing’ | 26 (0.11%) |
| Tenure status of households: | |
| Illegal Settlement | 191 (0.80%) |
| Others | 562 (2.34%) |
| Owner/Transferred dwellings/Pays Dividends | 7502 (31.3%) |
| Renting | 3872 (16.1%) |
| Stays temporarily with a relative | 10646 (44.4%) |
| ‘Missing’ | 1206 (5.03%) |
| Cum. Days of Treatment (1st Treatment) | 147 [84.0;254] |
| Cum. Days of Treatment (2nd Treatment) | 318 [207;489] |
| Cum. Days of Treatment (3rd Treatment) | 485 [335;704] |
| Cum. Days of Treatment (4th Treatment) | 638 [433;897] |
| Cum. Days of Treatment (5th Treatment) | 805 [550;1041] |
| Cum. Days of Treatment (6th Treatment) | 944 [716;1152] |
| Cum. Days of Treatment (7th Treatment) | 1076 [888;1279] |
| Cum. Days of Treatment (8th Treatment) | 1192 [1152;1232] |
| Cum. Days of Treatment (9th Treatment) | 1403 [1403;1403] |
| Cum. Days of Treatment (10th Treatment) | 1622 [1622;1622] |
| Days of Treatment (1st Treatment) | 147 [84.0;254] |
| Days of Treatment (2nd Treatment) | 137 [77.0;239] |
| Days of Treatment (3rd Treatment) | 134 [73.0;237] |
| Days of Treatment (4th Treatment) | 126 [70.0;224] |
| Days of Treatment (5th Treatment) | 143 [67.0;260] |
| Days of Treatment (6th Treatment) | 145 [76.8;199] |
| Days of Treatment (7th Treatment) | 120 [23.0;175] |
| Days of Treatment (8th Treatment) | 40.0 [29.5;84.5] |
| Days of Treatment (9th Treatment) | 211 [211;211] |
| Days of Treatment (10th Treatment) | 219 [219;219] |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] |
| Cum. Diff Between Treatments (2nd Treatment) | 801 [405;1390] |
| Cum. Diff Between Treatments (3rd Treatment) | 1155 [686;1680] |
| Cum. Diff Between Treatments (4th Treatment) | 1341 [886;1972] |
| Cum. Diff Between Treatments (5th Treatment) | 1458 [1047;1970] |
| Cum. Diff Between Treatments (6th Treatment) | 1509 [1160;2294] |
| Cum. Diff Between Treatments (7th Treatment) | 1188 [1184;1422] |
| Cum. Diff Between Treatments (8th Treatment) | 1706 [1706;1706] |
| Cum. Diff Between Treatments (9th Treatment) | 1944 [1944;1944] |
| Cum. Diff Between Treatments (10th Treatment) | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | |
| 1 | 18472 (77.0%) |
| 2 | 3908 (16.3%) |
| 3 | 1087 (4.53%) |
| 4 | 347 (1.45%) |
| 5 | 111 (0.46%) |
| 6 | 37 (0.15%) |
| 7 | 14 (0.06%) |
| 8 | 2 (0.01%) |
| 10 | 1 (0.00%) |
| Max. Cumulative Days of Treatment | 182 [98.0;332] |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] |
| Total count of Psychiatric & Drug dependence Diagnostics | 1.00 [1.00;2.00] |
| cnt_mod_cie_10_or: | |
| 0 | 9259 (38.6%) |
| 1 | 14314 (59.7%) |
| 2 | 365 (1.52%) |
| 3 | 41 (0.17%) |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | |
| 0 | 14457 (60.3%) |
| 1 | 9116 (38.0%) |
| 2 | 365 (1.52%) |
| 3 | 41 (0.17%) |
| Days of Treatment (Fourth or those that follow) | 138 [78.8;227] |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | 277 [140;485] |
| Cumulative Days of Treatment (Fourth or those that follow) | 692 [460;936] |
| Cumulative Difference Between Treatments (Fifth or those that follow) | 1412 [1031;1995] |
| Average Cumulative Days of Treatment (Fourth or those that follow) | 163 [110;221] |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | 340 [236;488] |
| Comorbidity ICD-10 (with amount of different diagnosis): | |
| Without psychiatric comorbidity | 9259 (38.6%) |
| Diagnosis unknown (under study) | 5198 (21.7%) |
| One | 9116 (38.0%) |
| Two or more | 406 (1.69%) |
| No. of treatments with 18+ at admission between 2010 and 2019: | |
| 01 | 18475 (77.0%) |
| 02 | 3907 (16.3%) |
| 03 | 1085 (4.52%) |
| 04 or more | 512 (2.14%) |
| Sexual abuse: | |
| No sexual abuse | 18880 (78.7%) |
| Sexual abuse | 289 (1.21%) |
| ‘Missing’ | 4810 (20.1%) |
| Domestic violence: | |
| No domestic violence | 14304 (59.7%) |
| Domestic violence | 4865 (20.3%) |
| ‘Missing’ | 4810 (20.1%) |
| Note. Variables of C1 dataset had to be standardized before comparison; | |
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | |
| Categorical variables are presented as number (%) |
Primary substance
Show code
table4 <- compareGroups::compareGroups(sus_principal_mod ~ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+ cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+ dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence= 3
),
data = prueba2,
include.miss = T,
var.equal=T
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table4)
#p.adjust(pvals, method = "BH")
restab4 <- createTable(table4, show.p.overall = T)
compareGroups::export2md(restab4, size=9, first.strip=T, hide.no="no", position="center",
format="html",caption= "Summary descriptives by Primary Substance at Admission")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")| Alcohol | Cocaine hydrochloride | Marijuana | Other | Cocaine paste | p.overall | |
|---|---|---|---|---|---|---|
| N=4142 | N=5394 | N=2872 | N=295 | N=11276 | ||
| Sexo Usuario/Sex of User: | <0.001 | |||||
| Men | 3321 (80.2%) | 4474 (82.9%) | 2356 (82.0%) | 223 (75.6%) | 9476 (84.0%) | |
| Women | 821 (19.8%) | 920 (17.1%) | 516 (18.0%) | 72 (24.4%) | 1800 (16.0%) | |
| escolaridad_rec: | . | |||||
| 3-Completed primary school or less | 900 (21.7%) | 873 (16.2%) | 536 (18.7%) | 62 (21.0%) | 3263 (28.9%) | |
| 2-Completed high school or less | 2376 (57.4%) | 3441 (63.8%) | 1714 (59.7%) | 152 (51.5%) | 6835 (60.6%) | |
| 1-More than high school | 852 (20.6%) | 1063 (19.7%) | 614 (21.4%) | 79 (26.8%) | 1143 (10.1%) | |
| ‘Missing’ | 14 (0.34%) | 17 (0.32%) | 8 (0.28%) | 2 (0.68%) | 35 (0.31%) | |
| Biopsychosocial Compromise: | <0.001 | |||||
| 1-Mild | 647 (15.6%) | 538 (9.97%) | 442 (15.4%) | 19 (6.44%) | 594 (5.27%) | |
| 2-Moderate | 2601 (62.8%) | 3339 (61.9%) | 1795 (62.5%) | 156 (52.9%) | 6058 (53.7%) | |
| 3-Severe | 814 (19.7%) | 1406 (26.1%) | 560 (19.5%) | 115 (39.0%) | 4457 (39.5%) | |
| ‘Missing’ | 80 (1.93%) | 111 (2.06%) | 75 (2.61%) | 5 (1.69%) | 167 (1.48%) | |
| Estado Conyugal/Marital Status: | . | |||||
| Married/Shared living arrangements | 1019 (24.6%) | 1350 (25.0%) | 465 (16.2%) | 36 (12.2%) | 2257 (20.0%) | |
| Separated/Divorced | 108 (2.61%) | 128 (2.37%) | 48 (1.67%) | 8 (2.71%) | 209 (1.85%) | |
| Single | 3005 (72.5%) | 3908 (72.5%) | 2352 (81.9%) | 250 (84.7%) | 8779 (77.9%) | |
| Widower | 5 (0.12%) | 3 (0.06%) | 4 (0.14%) | 1 (0.34%) | 17 (0.15%) | |
| ‘Missing’ | 5 (0.12%) | 5 (0.09%) | 3 (0.10%) | 0 (0.00%) | 14 (0.12%) | |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 15.0 [13.0;16.0] | 15.0 [13.0;16.0] | 15.0 [13.0;16.0] | 14.0 [13.0;16.0] | 14.0 [13.0;16.0] | <0.001 |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 25.7 [23.1;27.9] | 25.6 [23.0;27.8] | 23.6 [21.2;26.5] | 24.3 [21.1;27.8] | 25.5 [22.8;27.8] | <0.001 |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | 0.000 | |||||
| Alcohol | 3256 (78.6%) | 2366 (43.9%) | 982 (34.2%) | 79 (26.8%) | 3658 (32.4%) | |
| Cocaine hydrochloride | 44 (1.06%) | 604 (11.2%) | 22 (0.77%) | 4 (1.36%) | 260 (2.31%) | |
| Cocaine paste | 28 (0.68%) | 57 (1.06%) | 32 (1.11%) | 2 (0.68%) | 935 (8.29%) | |
| Marijuana | 513 (12.4%) | 1913 (35.5%) | 1490 (51.9%) | 121 (41.0%) | 5128 (45.5%) | |
| Other | 43 (1.04%) | 65 (1.21%) | 41 (1.43%) | 65 (22.0%) | 197 (1.75%) | |
| ‘Missing’ | 258 (6.23%) | 389 (7.21%) | 305 (10.6%) | 24 (8.14%) | 1098 (9.74%) | |
| Starting Substance: | . | |||||
| Alcohol | 3249 (78.4%) | 2335 (43.3%) | 973 (33.9%) | 79 (26.8%) | 3546 (31.4%) | |
| Cocaine hydrochloride | 45 (1.09%) | 627 (11.6%) | 24 (0.84%) | 4 (1.36%) | 270 (2.39%) | |
| Marijuana | 520 (12.6%) | 1922 (35.6%) | 1498 (52.2%) | 121 (41.0%) | 5194 (46.1%) | |
| Other | 41 (0.99%) | 63 (1.17%) | 39 (1.36%) | 65 (22.0%) | 190 (1.68%) | |
| Cocaine paste | 29 (0.70%) | 58 (1.08%) | 33 (1.15%) | 2 (0.68%) | 978 (8.67%) | |
| ‘Missing’ | 258 (6.23%) | 389 (7.21%) | 305 (10.6%) | 24 (8.14%) | 1098 (9.74%) | |
| Frequency of drug use in the primary substance: | . | |||||
| 1 day a week or more | 384 (9.27%) | 502 (9.31%) | 125 (4.35%) | 14 (4.75%) | 591 (5.24%) | |
| 2 to 3 days a week | 1817 (43.9%) | 1934 (35.9%) | 639 (22.2%) | 52 (17.6%) | 2617 (23.2%) | |
| 4 to 6 days a week | 758 (18.3%) | 941 (17.4%) | 416 (14.5%) | 42 (14.2%) | 1948 (17.3%) | |
| Daily | 964 (23.3%) | 1630 (30.2%) | 1579 (55.0%) | 166 (56.3%) | 5572 (49.4%) | |
| Less than 1 day a week | 196 (4.73%) | 354 (6.56%) | 99 (3.45%) | 19 (6.44%) | 498 (4.42%) | |
| ‘Missing’ | 23 (0.56%) | 33 (0.61%) | 14 (0.49%) | 2 (0.68%) | 50 (0.44%) | |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | . | |||||
| Smoked or Pulmonary Aspiration | 0 (0.00%) | 0 (0.00%) | 2872 (100%) | 34 (11.5%) | 10996 (97.5%) | |
| Intranasal (powder aspiration) | 0 (0.00%) | 5394 (100%) | 0 (0.00%) | 14 (4.75%) | 167 (1.48%) | |
| Injected Intravenously or Intramuscularly | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 16 (5.42%) | 0 (0.00%) | |
| Oral (drunk or eaten) | 4142 (100%) | 0 (0.00%) | 0 (0.00%) | 225 (76.3%) | 103 (0.91%) | |
| Other | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 5 (1.69%) | 7 (0.06%) | |
| ‘Missing’ | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 1 (0.34%) | 3 (0.03%) | |
| Whom you live with(cohabitation status) (Recoded) (f): | <0.001 | |||||
| Alone | 311 (7.51%) | 199 (3.69%) | 148 (5.15%) | 16 (5.42%) | 661 (5.86%) | |
| Family of origin | 2434 (58.8%) | 3384 (62.7%) | 2031 (70.7%) | 222 (75.3%) | 7384 (65.5%) | |
| With couple/children | 1397 (33.7%) | 1811 (33.6%) | 693 (24.1%) | 57 (19.3%) | 3231 (28.7%) | |
| Number of Children (Max. Value), adding 1 if pregnant at admission | 1.00 [0.00;1.00] | 1.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 1.00 [0.00;2.00] | <0.001 |
| Occupational Status Corrected(f): | . | |||||
| Employed | 2371 (57.2%) | 2833 (52.5%) | 1284 (44.7%) | 96 (32.5%) | 3839 (34.0%) | |
| Inactive | 410 (9.90%) | 368 (6.82%) | 418 (14.6%) | 43 (14.6%) | 780 (6.92%) | |
| Looking for a job for the first time | 16 (0.39%) | 14 (0.26%) | 22 (0.77%) | 2 (0.68%) | 43 (0.38%) | |
| No activity | 163 (3.94%) | 226 (4.19%) | 200 (6.96%) | 27 (9.15%) | 717 (6.36%) | |
| Not seeking for work | 20 (0.48%) | 48 (0.89%) | 24 (0.84%) | 5 (1.69%) | 119 (1.06%) | |
| Unemployed | 1162 (28.1%) | 1905 (35.3%) | 924 (32.2%) | 122 (41.4%) | 5778 (51.2%) | |
| Occupational Category Corrected(f): | . | |||||
| Employer | 88 (2.12%) | 83 (1.54%) | 34 (1.18%) | 2 (0.68%) | 115 (1.02%) | |
| Other | 48 (1.16%) | 46 (0.85%) | 30 (1.04%) | 4 (1.36%) | 94 (0.83%) | |
| Salaried | 1618 (39.1%) | 1929 (35.8%) | 878 (30.6%) | 50 (16.9%) | 2564 (22.7%) | |
| Self-employed | 399 (9.63%) | 559 (10.4%) | 217 (7.56%) | 23 (7.80%) | 736 (6.53%) | |
| Unpaid family labour | 5 (0.12%) | 16 (0.30%) | 11 (0.38%) | 0 (0.00%) | 33 (0.29%) | |
| Volunteer worker | 7 (0.17%) | 8 (0.15%) | 9 (0.31%) | 1 (0.34%) | 14 (0.12%) | |
| ‘Missing’ | 1977 (47.7%) | 2753 (51.0%) | 1693 (58.9%) | 215 (72.9%) | 7720 (68.5%) | |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | <0.001 | |||||
| Mayor o igual a 90 días | 3214 (77.6%) | 3998 (74.1%) | 2184 (76.0%) | 221 (74.9%) | 7766 (68.9%) | |
| Menos de 90 días | 928 (22.4%) | 1396 (25.9%) | 688 (24.0%) | 74 (25.1%) | 3510 (31.1%) | |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | <0.001 | |||||
| Without psychiatric comorbidity | 1652 (39.9%) | 2156 (40.0%) | 1100 (38.3%) | 75 (25.4%) | 4276 (37.9%) | |
| Diagnosis unknown (under study) | 720 (17.4%) | 1141 (21.2%) | 568 (19.8%) | 59 (20.0%) | 2710 (24.0%) | |
| With psychiatric comorbidity | 1770 (42.7%) | 2097 (38.9%) | 1204 (41.9%) | 161 (54.6%) | 4290 (38.0%) | |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 165 [96.0;281] | 150 [87.0;251] | 157 [91.0;274] | 161 [89.0;298] | 135 [75.8;240] | <0.001 |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.005 |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | <0.001 |
| Type of Plan (Independently of the Program): | . | |||||
| PAB | 2120 (51.2%) | 2321 (43.0%) | 1380 (48.1%) | 88 (29.8%) | 3368 (29.9%) | |
| PAI | 1811 (43.7%) | 2605 (48.3%) | 1331 (46.3%) | 175 (59.3%) | 5468 (48.5%) | |
| PR | 208 (5.02%) | 465 (8.62%) | 157 (5.47%) | 31 (10.5%) | 2425 (21.5%) | |
| ‘Missing’ | 3 (0.07%) | 3 (0.06%) | 4 (0.14%) | 1 (0.34%) | 15 (0.13%) | |
| Tenure status of households: | . | |||||
| Illegal Settlement | 18 (0.43%) | 22 (0.41%) | 22 (0.77%) | 3 (1.02%) | 126 (1.12%) | |
| Others | 88 (2.12%) | 109 (2.02%) | 90 (3.13%) | 4 (1.36%) | 271 (2.40%) | |
| Owner/Transferred dwellings/Pays Dividends | 1403 (33.9%) | 1454 (27.0%) | 945 (32.9%) | 90 (30.5%) | 3610 (32.0%) | |
| Renting | 821 (19.8%) | 941 (17.4%) | 445 (15.5%) | 53 (18.0%) | 1612 (14.3%) | |
| Stays temporarily with a relative | 1648 (39.8%) | 2647 (49.1%) | 1216 (42.3%) | 135 (45.8%) | 5000 (44.3%) | |
| ‘Missing’ | 164 (3.96%) | 221 (4.10%) | 154 (5.36%) | 10 (3.39%) | 657 (5.83%) | |
| Cum. Days of Treatment (1st Treatment) | 165 [96.0;281] | 150 [87.2;251] | 157 [91.0;274] | 161 [89.0;300] | 135 [76.0;240] | <0.001 |
| Cum. Days of Treatment (2nd Treatment) | 367 [235;577] | 320 [212;491] | 351 [239;520] | 402 [288;592] | 305 [196;468] | <0.001 |
| Cum. Days of Treatment (3rd Treatment) | 550 [383;784] | 483 [344;704] | 500 [384;676] | 600 [411;820] | 473 [320;690] | 0.013 |
| Cum. Days of Treatment (4th Treatment) | 810 [573;994] | 659 [378;840] | 595 [437;780] | 913 [766;1162] | 608 [432;874] | 0.013 |
| Cum. Days of Treatment (5th Treatment) | 597 [430;972] | 854 [506;1000] | 866 [776;925] | 2000 [2000;2000] | 786 [554;1067] | 0.364 |
| Cum. Days of Treatment (6th Treatment) | 1022 [669;1087] | 803 [727;1080] | 984 [940;995] | 2230 [2230;2230] | 949 [714;1175] | 0.471 |
| Cum. Days of Treatment (7th Treatment) | 876 [778;975] | 887 [581;957] | 1348 [1348;1348] | . [.;.] | 1178 [1029;1300] | 0.101 |
| Cum. Days of Treatment (8th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1192 [1152;1232] | . |
| Cum. Days of Treatment (9th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1403 [1403;1403] | . |
| Cum. Days of Treatment (10th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1622 [1622;1622] | . |
| Days of Treatment (1st Treatment) | 165 [96.0;281] | 150 [87.0;251] | 157 [91.0;274] | 161 [89.0;298] | 135 [75.8;240] | <0.001 |
| Days of Treatment (2nd Treatment) | 160 [92.0;278] | 139 [79.0;241] | 147 [80.2;262] | 215 [106;307] | 129 [72.0;224] | <0.001 |
| Days of Treatment (3rd Treatment) | 135 [81.0;232] | 140 [75.0;223] | 138 [73.2;251] | 108 [33.0;189] | 133 [72.0;239] | 0.881 |
| Days of Treatment (4th Treatment) | 152 [70.2;336] | 124 [61.5;217] | 128 [80.5;206] | 75.0 [46.5;195] | 124 [70.2;215] | 0.505 |
| Days of Treatment (5th Treatment) | 182 [76.8;232] | 151 [74.5;226] | 330 [213;388] | 588 [588;588] | 141 [63.0;254] | 0.268 |
| Days of Treatment (6th Treatment) | 67.0 [64.0;175] | 85.0 [36.5;180] | 144 [130;195] | 230 [230;230] | 154 [106;198] | 0.373 |
| Days of Treatment (7th Treatment) | 31.0 [20.5;41.5] | 175 [99.0;215] | 364 [364;364] | . [.;.] | 120 [28.0;146] | 0.223 |
| Days of Treatment (8th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 40.0 [29.5;84.5] | . |
| Days of Treatment (9th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 211 [211;211] | . |
| Days of Treatment (10th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 219 [219;219] | . |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;74.0] | <0.001 |
| Cum. Diff Between Treatments (2nd Treatment) | 812 [413;1310] | 799 [405;1420] | 950 [533;1661] | 947 [733;1511] | 774 [392;1366] | 0.120 |
| Cum. Diff Between Treatments (3rd Treatment) | 1075 [645;1639] | 1221 [794;1732] | 1209 [696;1821] | 1274 [858;1290] | 1146 [680;1667] | 0.867 |
| Cum. Diff Between Treatments (4th Treatment) | 1424 [1098;2117] | 1141 [888;2078] | 1428 [1187;1640] | 1398 [1398;1398] | 1384 [880;1978] | 0.971 |
| Cum. Diff Between Treatments (5th Treatment) | 1695 [960;1700] | 1497 [1072;2131] | 1446 [1432;1668] | 1415 [1415;1415] | 1470 [1062;1992] | 0.992 |
| Cum. Diff Between Treatments (6th Treatment) | 1628 [1295;1961] | 2411 [1953;2594] | 1827 [1827;1827] | . [.;.] | 1287 [1088;1906] | 0.452 |
| Cum. Diff Between Treatments (7th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1188 [1184;1422] | . |
| Cum. Diff Between Treatments (8th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1706 [1706;1706] | . |
| Cum. Diff Between Treatments (9th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1944 [1944;1944] | . |
| Cum. Diff Between Treatments (10th Treatment) | . | . | . | . | . | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | . | |||||
| 1 | 3435 (82.9%) | 4367 (81.0%) | 2422 (84.3%) | 246 (83.4%) | 8002 (71.0%) | |
| 2 | 537 (13.0%) | 781 (14.5%) | 328 (11.4%) | 35 (11.9%) | 2227 (19.7%) | |
| 3 | 116 (2.80%) | 176 (3.26%) | 91 (3.17%) | 11 (3.73%) | 693 (6.15%) | |
| 4 | 46 (1.11%) | 43 (0.80%) | 25 (0.87%) | 2 (0.68%) | 231 (2.05%) | |
| 5 | 3 (0.07%) | 17 (0.32%) | 3 (0.10%) | 0 (0.00%) | 88 (0.78%) | |
| 6 | 3 (0.07%) | 7 (0.13%) | 2 (0.07%) | 1 (0.34%) | 24 (0.21%) | |
| 7 | 2 (0.05%) | 3 (0.06%) | 1 (0.03%) | 0 (0.00%) | 8 (0.07%) | |
| 8 | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 2 (0.02%) | |
| 10 | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 1 (0.01%) | |
| Max. Cumulative Days of Treatment | 194 [106;350] | 181 [99.0;313] | 182 [98.0;331] | 200 [95.0;337] | 179 [93.0;336] | <0.001 |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;123] | <0.001 |
| Total count of Psychiatric & Drug dependence Diagnostics: | . | |||||
| 0 | 1108 (26.8%) | 954 (17.7%) | 719 (25.0%) | 33 (11.2%) | 1450 (12.9%) | |
| 1 | 1965 (47.4%) | 2914 (54.0%) | 1377 (47.9%) | 138 (46.8%) | 6303 (55.9%) | |
| 2 | 1003 (24.2%) | 1438 (26.7%) | 745 (25.9%) | 115 (39.0%) | 3376 (29.9%) | |
| 3 | 60 (1.45%) | 75 (1.39%) | 28 (0.97%) | 9 (3.05%) | 137 (1.21%) | |
| 4 | 6 (0.14%) | 13 (0.24%) | 3 (0.10%) | 0 (0.00%) | 10 (0.09%) | |
| cnt_mod_cie_10_or: | . | |||||
| 0 | 1652 (39.9%) | 2156 (40.0%) | 1100 (38.3%) | 75 (25.4%) | 4276 (37.9%) | |
| 1 | 2405 (58.1%) | 3139 (58.2%) | 1729 (60.2%) | 207 (70.2%) | 6834 (60.6%) | |
| 2 | 74 (1.79%) | 84 (1.56%) | 38 (1.32%) | 13 (4.41%) | 156 (1.38%) | |
| 3 | 11 (0.27%) | 15 (0.28%) | 5 (0.17%) | 0 (0.00%) | 10 (0.09%) | |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | . | |||||
| 0 | 2372 (57.3%) | 3297 (61.1%) | 1668 (58.1%) | 134 (45.4%) | 6986 (62.0%) | |
| 1 | 1685 (40.7%) | 1998 (37.0%) | 1161 (40.4%) | 148 (50.2%) | 4124 (36.6%) | |
| 2 | 74 (1.79%) | 84 (1.56%) | 38 (1.32%) | 13 (4.41%) | 156 (1.38%) | |
| 3 | 11 (0.27%) | 15 (0.28%) | 5 (0.17%) | 0 (0.00%) | 10 (0.09%) | |
| Days of Treatment (Fourth or those that follow) | 149 [74.8;322] | 138 [74.0;218] | 144 [99.0;218] | 75.0 [46.5;226] | 136 [79.1;223] | 0.719 |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | 308 [170;552] | 340 [171;558] | 330 [138;365] | 70.5 [70.5;70.5] | 256 [132;483] | 0.695 |
| Cumulative Days of Treatment (Fourth or those that follow) | 831 [585;1012] | 676 [412;902] | 683 [499;809] | 913 [766;1397] | 684 [449;929] | 0.047 |
| Cumulative Difference Between Treatments (Fifth or those that follow) | 1694 [1127;2134] | 1370 [1032;2078] | 1520 [1385;1640] | 1406 [1406;1406] | 1411 [1018;2019] | 0.940 |
| Average Cumulative Days of Treatment (Fourth or those that follow) | 202 [144;248] | 161 [98.3;201] | 149 [125;192] | 228 [191;302] | 159 [108;217] | 0.014 |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | 356 [272;518] | 295 [241;493] | 347 [296;407] | 316 [316;316] | 341 [228;483] | 0.990 |
| Comorbidity ICD-10 (with amount of different diagnosis): | . | |||||
| Without psychiatric comorbidity | 1652 (39.9%) | 2156 (40.0%) | 1100 (38.3%) | 75 (25.4%) | 4276 (37.9%) | |
| Diagnosis unknown (under study) | 720 (17.4%) | 1141 (21.2%) | 568 (19.8%) | 59 (20.0%) | 2710 (24.0%) | |
| One | 1685 (40.7%) | 1998 (37.0%) | 1161 (40.4%) | 148 (50.2%) | 4124 (36.6%) | |
| Two or more | 85 (2.05%) | 99 (1.84%) | 43 (1.50%) | 13 (4.41%) | 166 (1.47%) | |
| No. of treatments with 18+ at admission between 2010 and 2019: | <0.001 | |||||
| 01 | 3435 (82.9%) | 4368 (81.0%) | 2422 (84.3%) | 246 (83.4%) | 8004 (71.0%) | |
| 02 | 537 (13.0%) | 781 (14.5%) | 328 (11.4%) | 36 (12.2%) | 2225 (19.7%) | |
| 03 | 116 (2.80%) | 175 (3.24%) | 91 (3.17%) | 10 (3.39%) | 693 (6.15%) | |
| 04 or more | 54 (1.30%) | 70 (1.30%) | 31 (1.08%) | 3 (1.02%) | 354 (3.14%) | |
| Sexual abuse: | . | |||||
| No sexual abuse | 3147 (76.0%) | 4124 (76.5%) | 2263 (78.8%) | 227 (76.9%) | 9119 (80.9%) | |
| Sexual abuse | 60 (1.45%) | 65 (1.21%) | 23 (0.80%) | 6 (2.03%) | 135 (1.20%) | |
| ‘Missing’ | 935 (22.6%) | 1205 (22.3%) | 586 (20.4%) | 62 (21.0%) | 2022 (17.9%) | |
| Domestic violence: | <0.001 | |||||
| No domestic violence | 2278 (55.0%) | 3228 (59.8%) | 1764 (61.4%) | 161 (54.6%) | 6873 (61.0%) | |
| Domestic violence | 929 (22.4%) | 961 (17.8%) | 522 (18.2%) | 72 (24.4%) | 2381 (21.1%) | |
| ‘Missing’ | 935 (22.6%) | 1205 (22.3%) | 586 (20.4%) | 62 (21.0%) | 2022 (17.9%) | |
| Note. Variables of C1 dataset had to be standardized before comparison; | ||||||
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | ||||||
| Categorical variables are presented as number (%) |
Living with
Show code
table5 <- compareGroups::compareGroups(con_quien_vive_joel ~ sus_principal_mod+ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(sus_principal_mod=3,
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = prueba2,
include.miss = T,
var.equal=T,
max.xlev = 10,
max.ylev = 10
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table5)
#p.adjust(pvals, method = "BH")
restab5 <- createTable(table5, show.p.overall = T)
compareGroups::export2md(restab5, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Alone","Family of origin", "With couple", "P-value"),
format="html",caption= "Summary descriptives by With whom they live")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")| Variables | Alone | Family of origin | With couple | P-value |
|---|---|---|---|---|
| N=1335 | N=15455 | N=7189 | ||
| Sustancia Principal de Consumo (Sólo más frecuentes)(f)/Primary or Main Substance of Consumption at Admission (Only more frequent)(f): | <0.001 | |||
| Alcohol | 311 (23.3%) | 2434 (15.7%) | 1397 (19.4%) | |
| Cocaine hydrochloride | 199 (14.9%) | 3384 (21.9%) | 1811 (25.2%) | |
| Marijuana | 148 (11.1%) | 2031 (13.1%) | 693 (9.64%) | |
| Other | 16 (1.20%) | 222 (1.44%) | 57 (0.79%) | |
| Cocaine paste | 661 (49.5%) | 7384 (47.8%) | 3231 (44.9%) | |
| Sexo Usuario/Sex of User: | <0.001 | |||
| Men | 1171 (87.7%) | 13154 (85.1%) | 5525 (76.9%) | |
| Women | 164 (12.3%) | 2301 (14.9%) | 1664 (23.1%) | |
| escolaridad_rec: | . | |||
| 3-Completed primary school or less | 359 (26.9%) | 3333 (21.6%) | 1942 (27.0%) | |
| 2-Completed high school or less | 767 (57.5%) | 9420 (61.0%) | 4331 (60.2%) | |
| 1-More than high school | 201 (15.1%) | 2660 (17.2%) | 890 (12.4%) | |
| ‘Missing’ | 8 (0.60%) | 42 (0.27%) | 26 (0.36%) | |
| Biopsychosocial Compromise: | <0.001 | |||
| 1-Mild | 92 (6.89%) | 1328 (8.59%) | 820 (11.4%) | |
| 2-Moderate | 642 (48.1%) | 8856 (57.3%) | 4451 (61.9%) | |
| 3-Severe | 588 (44.0%) | 4988 (32.3%) | 1776 (24.7%) | |
| ‘Missing’ | 13 (0.97%) | 283 (1.83%) | 142 (1.98%) | |
| Estado Conyugal/Marital Status: | . | |||
| Married/Shared living arrangements | 101 (7.57%) | 858 (5.55%) | 4168 (58.0%) | |
| Separated/Divorced | 55 (4.12%) | 361 (2.34%) | 85 (1.18%) | |
| Single | 1171 (87.7%) | 14203 (91.9%) | 2920 (40.6%) | |
| Widower | 1 (0.07%) | 24 (0.16%) | 5 (0.07%) | |
| ‘Missing’ | 7 (0.52%) | 9 (0.06%) | 11 (0.15%) | |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 14.0 [13.0;16.0] | 15.0 [13.0;16.0] | 15.0 [13.0;16.0] | <0.001 |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 26.3 [23.5;28.3] | 24.7 [22.0;27.3] | 26.4 [24.1;28.3] | <0.001 |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | <0.001 | |||
| Alcohol | 587 (44.0%) | 6505 (42.1%) | 3249 (45.2%) | |
| Cocaine hydrochloride | 37 (2.77%) | 548 (3.55%) | 349 (4.85%) | |
| Cocaine paste | 68 (5.09%) | 636 (4.12%) | 350 (4.87%) | |
| Marijuana | 511 (38.3%) | 6157 (39.8%) | 2497 (34.7%) | |
| Other | 26 (1.95%) | 250 (1.62%) | 135 (1.88%) | |
| ‘Missing’ | 106 (7.94%) | 1359 (8.79%) | 609 (8.47%) | |
| Starting Substance: | <0.001 | |||
| Alcohol | 581 (43.5%) | 6385 (41.3%) | 3216 (44.7%) | |
| Cocaine hydrochloride | 39 (2.92%) | 571 (3.69%) | 360 (5.01%) | |
| Marijuana | 514 (38.5%) | 6228 (40.3%) | 2513 (35.0%) | |
| Other | 24 (1.80%) | 245 (1.59%) | 129 (1.79%) | |
| Cocaine paste | 71 (5.32%) | 667 (4.32%) | 362 (5.04%) | |
| ‘Missing’ | 106 (7.94%) | 1359 (8.79%) | 609 (8.47%) | |
| Frequency of drug use in the primary substance: | <0.001 | |||
| 1 day a week or more | 55 (4.12%) | 904 (5.85%) | 657 (9.14%) | |
| 2 to 3 days a week | 328 (24.6%) | 4334 (28.0%) | 2397 (33.3%) | |
| 4 to 6 days a week | 204 (15.3%) | 2737 (17.7%) | 1164 (16.2%) | |
| Daily | 699 (52.4%) | 6768 (43.8%) | 2444 (34.0%) | |
| Less than 1 day a week | 45 (3.37%) | 633 (4.10%) | 488 (6.79%) | |
| ‘Missing’ | 4 (0.30%) | 79 (0.51%) | 39 (0.54%) | |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | . | |||
| Smoked or Pulmonary Aspiration | 798 (59.8%) | 9266 (60.0%) | 3838 (53.4%) | |
| Intranasal (powder aspiration) | 205 (15.4%) | 3505 (22.7%) | 1865 (25.9%) | |
| Injected Intravenously or Intramuscularly | 2 (0.15%) | 12 (0.08%) | 2 (0.03%) | |
| Oral (drunk or eaten) | 328 (24.6%) | 2662 (17.2%) | 1480 (20.6%) | |
| Other | 1 (0.07%) | 7 (0.05%) | 4 (0.06%) | |
| ‘Missing’ | 1 (0.07%) | 3 (0.02%) | 0 (0.00%) | |
| Number of Children (Max. Value), adding 1 if pregnant at admission | 1.00 [0.00;1.00] | 0.00 [0.00;1.00] | 1.00 [1.00;2.00] | 0.000 |
| Occupational Status Corrected(f): | <0.001 | |||
| Employed | 648 (48.5%) | 5601 (36.2%) | 4174 (58.1%) | |
| Inactive | 57 (4.27%) | 1231 (7.97%) | 731 (10.2%) | |
| Looking for a job for the first time | 3 (0.22%) | 78 (0.50%) | 16 (0.22%) | |
| No activity | 86 (6.44%) | 1015 (6.57%) | 232 (3.23%) | |
| Not seeking for work | 34 (2.55%) | 154 (1.00%) | 28 (0.39%) | |
| Unemployed | 507 (38.0%) | 7376 (47.7%) | 2008 (27.9%) | |
| Occupational Category Corrected(f): | . | |||
| Employer | 14 (1.05%) | 163 (1.05%) | 145 (2.02%) | |
| Other | 17 (1.27%) | 128 (0.83%) | 77 (1.07%) | |
| Salaried | 432 (32.4%) | 3833 (24.8%) | 2774 (38.6%) | |
| Self-employed | 119 (8.91%) | 953 (6.17%) | 862 (12.0%) | |
| Unpaid family labour | 1 (0.07%) | 52 (0.34%) | 12 (0.17%) | |
| Volunteer worker | 2 (0.15%) | 25 (0.16%) | 12 (0.17%) | |
| ‘Missing’ | 750 (56.2%) | 10301 (66.7%) | 3307 (46.0%) | |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | <0.001 | |||
| Mayor o igual a 90 días | 865 (64.8%) | 11229 (72.7%) | 5289 (73.6%) | |
| Menos de 90 días | 470 (35.2%) | 4226 (27.3%) | 1900 (26.4%) | |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | <0.001 | |||
| Without psychiatric comorbidity | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| Diagnosis unknown (under study) | 346 (25.9%) | 3322 (21.5%) | 1530 (21.3%) | |
| With psychiatric comorbidity | 536 (40.1%) | 6415 (41.5%) | 2571 (35.8%) | |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 125 [68.0;223] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.015 |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | <0.001 |
| Type of Plan (Independently of the Program): | . | |||
| PAB | 391 (29.3%) | 5506 (35.6%) | 3380 (47.0%) | |
| PAI | 586 (43.9%) | 7508 (48.6%) | 3296 (45.8%) | |
| PR | 356 (26.7%) | 2423 (15.7%) | 507 (7.05%) | |
| ‘Missing’ | 2 (0.15%) | 18 (0.12%) | 6 (0.08%) | |
| Tenure status of households: | 0.000 | |||
| Illegal Settlement | 53 (3.97%) | 48 (0.31%) | 90 (1.25%) | |
| Others | 74 (5.54%) | 374 (2.42%) | 114 (1.59%) | |
| Owner/Transferred dwellings/Pays Dividends | 258 (19.3%) | 5345 (34.6%) | 1899 (26.4%) | |
| Renting | 555 (41.6%) | 1154 (7.47%) | 2163 (30.1%) | |
| Stays temporarily with a relative | 145 (10.9%) | 7879 (51.0%) | 2622 (36.5%) | |
| ‘Missing’ | 250 (18.7%) | 655 (4.24%) | 301 (4.19%) | |
| Cum. Days of Treatment (1st Treatment) | 125 [68.0;224] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Cum. Days of Treatment (2nd Treatment) | 285 [174;423] | 320 [206;493] | 326 [218;489] | <0.001 |
| Cum. Days of Treatment (3rd Treatment) | 463 [260;644] | 474 [326;700] | 522 [361;730] | 0.008 |
| Cum. Days of Treatment (4th Treatment) | 526 [339;755] | 633 [426;878] | 671 [486;941] | 0.059 |
| Cum. Days of Treatment (5th Treatment) | 690 [532;1120] | 798 [541;1047] | 876 [576;1021] | 0.604 |
| Cum. Days of Treatment (6th Treatment) | 460 [356;565] | 897 [712;1153] | 1006 [868;1144] | 0.106 |
| Cum. Days of Treatment (7th Treatment) | 477 [376;578] | 1165 [993;1331] | 1076 [1074;1254] | 0.100 |
| Cum. Days of Treatment (8th Treatment) | . [.;.] | 1152 [1131;1172] | 1273 [1273;1273] | 0.221 |
| Cum. Days of Treatment (9th Treatment) | . [.;.] | 1403 [1403;1403] | . [.;.] | . |
| Cum. Days of Treatment (10th Treatment) | . [.;.] | 1622 [1622;1622] | . [.;.] | . |
| Days of Treatment (1st Treatment) | 125 [68.0;223] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Days of Treatment (2nd Treatment) | 119 [57.0;204] | 137 [76.0;239] | 139 [83.0;245] | <0.001 |
| Days of Treatment (3rd Treatment) | 127 [71.0;220] | 127 [70.0;229] | 148 [84.8;246] | 0.020 |
| Days of Treatment (4th Treatment) | 143 [57.2;248] | 125 [70.0;215] | 131 [71.0;231] | 0.997 |
| Days of Treatment (5th Treatment) | 199 [139;335] | 138 [67.8;254] | 145 [59.2;260] | 0.567 |
| Days of Treatment (6th Treatment) | 28.0 [17.5;38.5] | 149 [121;195] | 116 [68.5;205] | 0.121 |
| Days of Treatment (7th Treatment) | 16.5 [13.2;19.8] | 169 [58.5;244] | 53.0 [52.0;120] | 0.075 |
| Days of Treatment (8th Treatment) | . [.;.] | 84.5 [62.2;107] | 19.0 [19.0;19.0] | 0.221 |
| Days of Treatment (9th Treatment) | . [.;.] | 211 [211;211] | . [.;.] | . |
| Days of Treatment (10th Treatment) | . [.;.] | 219 [219;219] | . [.;.] | . |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.812 |
| Cum. Diff Between Treatments (2nd Treatment) | 715 [380;1315] | 779 [396;1355] | 872 [441;1492] | 0.075 |
| Cum. Diff Between Treatments (3rd Treatment) | 920 [405;1812] | 1128 [674;1648] | 1235 [770;1697] | 0.436 |
| Cum. Diff Between Treatments (4th Treatment) | 1290 [1044;2121] | 1196 [843;1722] | 1448 [1149;2190] | 0.034 |
| Cum. Diff Between Treatments (5th Treatment) | 2020 [1858;2182] | 1470 [1091;1768] | 1418 [944;2384] | 0.513 |
| Cum. Diff Between Treatments (6th Treatment) | 2536 [2415;2657] | 1576 [1244;2085] | 1221 [1015;1287] | 0.174 |
| Cum. Diff Between Treatments (7th Treatment) | . [.;.] | 1418 [1300;1537] | 1188 [1188;1188] | 1.000 |
| Cum. Diff Between Treatments (8th Treatment) | . [.;.] | 1706 [1706;1706] | . [.;.] | . |
| Cum. Diff Between Treatments (9th Treatment) | . [.;.] | 1944 [1944;1944] | . [.;.] | . |
| Cum. Diff Between Treatments (10th Treatment) | . | . | . | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | . | |||
| 1 | 1033 (77.4%) | 11903 (77.0%) | 5536 (77.0%) | |
| 2 | 207 (15.5%) | 2540 (16.4%) | 1161 (16.1%) | |
| 3 | 71 (5.32%) | 673 (4.35%) | 343 (4.77%) | |
| 4 | 15 (1.12%) | 233 (1.51%) | 99 (1.38%) | |
| 5 | 7 (0.52%) | 69 (0.45%) | 35 (0.49%) | |
| 6 | 0 (0.00%) | 27 (0.17%) | 10 (0.14%) | |
| 7 | 2 (0.15%) | 8 (0.05%) | 4 (0.06%) | |
| 8 | 0 (0.00%) | 1 (0.01%) | 1 (0.01%) | |
| 10 | 0 (0.00%) | 1 (0.01%) | 0 (0.00%) | |
| Max. Cumulative Days of Treatment | 154 [80.0;304] | 183 [98.0;332] | 186 [100;338] | <0.001 |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.883 |
| Total count of Psychiatric & Drug dependence Diagnostics: | . | |||
| 0 | 224 (16.8%) | 2493 (16.1%) | 1547 (21.5%) | |
| 1 | 695 (52.1%) | 8175 (52.9%) | 3827 (53.2%) | |
| 2 | 399 (29.9%) | 4536 (29.3%) | 1742 (24.2%) | |
| 3 | 16 (1.20%) | 226 (1.46%) | 67 (0.93%) | |
| 4 | 1 (0.07%) | 25 (0.16%) | 6 (0.08%) | |
| cnt_mod_cie_10_or: | . | |||
| 0 | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| 1 | 860 (64.4%) | 9441 (61.1%) | 4013 (55.8%) | |
| 2 | 19 (1.42%) | 264 (1.71%) | 82 (1.14%) | |
| 3 | 3 (0.22%) | 32 (0.21%) | 6 (0.08%) | |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | . | |||
| 0 | 799 (59.9%) | 9040 (58.5%) | 4618 (64.2%) | |
| 1 | 514 (38.5%) | 6119 (39.6%) | 2483 (34.5%) | |
| 2 | 19 (1.42%) | 264 (1.71%) | 82 (1.14%) | |
| 3 | 3 (0.22%) | 32 (0.21%) | 6 (0.08%) | |
| Days of Treatment (Fourth or those that follow) | 149 [51.8;234] | 138 [77.8;225] | 137 [85.4;236] | 0.966 |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | 464 [238;588] | 258 [112;416] | 288 [164;682] | 0.064 |
| Cumulative Days of Treatment (Fourth or those that follow) | 590 [364;936] | 683 [454;932] | 727 [518;999] | 0.083 |
| Cumulative Difference Between Treatments (Fifth or those that follow) | 1760 [1044;2121] | 1345 [943;1759] | 1518 [1174;2252] | 0.081 |
| Average Cumulative Days of Treatment (Fourth or those that follow) | 133 [91.0;209] | 162 [107;218] | 171 [116;229] | 0.080 |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | 348 [261;497] | 312 [222;424] | 373 [286;544] | 0.064 |
| Comorbidity ICD-10 (with amount of different diagnosis): | <0.001 | |||
| Without psychiatric comorbidity | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| Diagnosis unknown (under study) | 346 (25.9%) | 3322 (21.5%) | 1530 (21.3%) | |
| One | 514 (38.5%) | 6119 (39.6%) | 2483 (34.5%) | |
| Two or more | 22 (1.65%) | 296 (1.92%) | 88 (1.22%) | |
| No. of treatments with 18+ at admission between 2010 and 2019: | 0.426 | |||
| 01 | 1034 (77.5%) | 11905 (77.0%) | 5536 (77.0%) | |
| 02 | 206 (15.4%) | 2540 (16.4%) | 1161 (16.1%) | |
| 03 | 71 (5.32%) | 671 (4.34%) | 343 (4.77%) | |
| 04 or more | 24 (1.80%) | 339 (2.19%) | 149 (2.07%) | |
| Sexual abuse: | 0.034 | |||
| No sexual abuse | 1053 (78.9%) | 12207 (79.0%) | 5620 (78.2%) | |
| Sexual abuse | 27 (2.02%) | 175 (1.13%) | 87 (1.21%) | |
| ‘Missing’ | 255 (19.1%) | 3073 (19.9%) | 1482 (20.6%) | |
| Domestic violence: | <0.001 | |||
| No domestic violence | 745 (55.8%) | 9509 (61.5%) | 4050 (56.3%) | |
| Domestic violence | 335 (25.1%) | 2873 (18.6%) | 1657 (23.0%) | |
| ‘Missing’ | 255 (19.1%) | 3073 (19.9%) | 1482 (20.6%) | |
| Note. Variables of C1 dataset had to be standardized before comparison; | ||||
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | ||||
| Categorical variables are presented as number (%) |
Readmissions
Show code
table6 <- compareGroups::compareGroups(had_readm ~ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = prueba2,
include.miss = T,
var.equal=T
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table6)
#p.adjust(pvals, method = "BH")
restab6 <- createTable(table6, show.p.overall = T)
compareGroups::export2md(restab6, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Had no Readmissions","Had Readmissions", "P-value"),
format="html",caption= "Table 5. Summary descriptives by Readmissions")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")| Variables | Had no Readmissions | Had Readmissions | P-value |
|---|---|---|---|
| N=18475 | N=5504 | ||
| Sexo Usuario/Sex of User: | 0.001 | ||
| Men | 15373 (83.2%) | 4477 (81.3%) | |
| Women | 3102 (16.8%) | 1027 (18.7%) | |
| escolaridad_rec: | <0.001 | ||
| 3-Completed primary school or less | 4166 (22.5%) | 1468 (26.7%) | |
| 2-Completed high school or less | 11046 (59.8%) | 3472 (63.1%) | |
| 1-More than high school | 3202 (17.3%) | 549 (9.97%) | |
| ‘Missing’ | 61 (0.33%) | 15 (0.27%) | |
| Biopsychosocial Compromise: | <0.001 | ||
| 1-Mild | 1840 (9.96%) | 400 (7.27%) | |
| 2-Moderate | 11058 (59.9%) | 2891 (52.5%) | |
| 3-Severe | 5244 (28.4%) | 2108 (38.3%) | |
| ‘Missing’ | 333 (1.80%) | 105 (1.91%) | |
| Estado Conyugal/Marital Status: | 0.105 | ||
| Married/Shared living arrangements | 3918 (21.2%) | 1209 (22.0%) | |
| Separated/Divorced | 397 (2.15%) | 104 (1.89%) | |
| Single | 14121 (76.4%) | 4173 (75.8%) | |
| Widower | 18 (0.10%) | 12 (0.22%) | |
| ‘Missing’ | 21 (0.11%) | 6 (0.11%) | |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 15.0 [13.0;16.0] | 14.0 [13.0;16.0] | 0.088 |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 25.3 [22.5;27.7] | 25.5 [22.8;27.8] | <0.001 |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | <0.001 | ||
| Alcohol | 7890 (42.7%) | 2451 (44.5%) | |
| Cocaine hydrochloride | 688 (3.72%) | 246 (4.47%) | |
| Cocaine paste | 667 (3.61%) | 387 (7.03%) | |
| Marijuana | 6926 (37.5%) | 2239 (40.7%) | |
| Other | 298 (1.61%) | 113 (2.05%) | |
| ‘Missing’ | 2006 (10.9%) | 68 (1.24%) | |
| Starting Substance: | <0.001 | ||
| Alcohol | 7839 (42.4%) | 2343 (42.6%) | |
| Cocaine hydrochloride | 703 (3.81%) | 267 (4.85%) | |
| Marijuana | 6951 (37.6%) | 2304 (41.9%) | |
| Other | 295 (1.60%) | 103 (1.87%) | |
| Cocaine paste | 681 (3.69%) | 419 (7.61%) | |
| ‘Missing’ | 2006 (10.9%) | 68 (1.24%) | |
| Frequency of drug use in the primary substance: | 0.003 | ||
| 1 day a week or more | 1272 (6.88%) | 344 (6.25%) | |
| 2 to 3 days a week | 5493 (29.7%) | 1566 (28.5%) | |
| 4 to 6 days a week | 3140 (17.0%) | 965 (17.5%) | |
| Daily | 7539 (40.8%) | 2372 (43.1%) | |
| Less than 1 day a week | 935 (5.06%) | 231 (4.20%) | |
| ‘Missing’ | 96 (0.52%) | 26 (0.47%) | |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | . | ||
| Smoked or Pulmonary Aspiration | 10250 (55.5%) | 3652 (66.4%) | |
| Intranasal (powder aspiration) | 4504 (24.4%) | 1071 (19.5%) | |
| Injected Intravenously or Intramuscularly | 15 (0.08%) | 1 (0.02%) | |
| Oral (drunk or eaten) | 3695 (20.0%) | 775 (14.1%) | |
| Other | 7 (0.04%) | 5 (0.09%) | |
| ‘Missing’ | 4 (0.02%) | 0 (0.00%) | |
| Whom you live with(cohabitation status) (Recoded) (f): | 0.935 | ||
| Alone | 1034 (5.60%) | 301 (5.47%) | |
| Family of origin | 11905 (64.4%) | 3550 (64.5%) | |
| With couple/children | 5536 (30.0%) | 1653 (30.0%) | |
| Number of Children (Max. Value), adding 1 if pregnant at admission | 1.00 [0.00;1.00] | 1.00 [0.00;2.00] | <0.001 |
| Occupational Status Corrected(f): | <0.001 | ||
| Employed | 8278 (44.8%) | 2145 (39.0%) | |
| Inactive | 1562 (8.45%) | 457 (8.30%) | |
| Looking for a job for the first time | 72 (0.39%) | 25 (0.45%) | |
| No activity | 1021 (5.53%) | 312 (5.67%) | |
| Not seeking for work | 157 (0.85%) | 59 (1.07%) | |
| Unemployed | 7385 (40.0%) | 2506 (45.5%) | |
| Occupational Category Corrected(f): | <0.001 | ||
| Employer | 256 (1.39%) | 66 (1.20%) | |
| Other | 176 (0.95%) | 46 (0.84%) | |
| Salaried | 5527 (29.9%) | 1512 (27.5%) | |
| Self-employed | 1584 (8.57%) | 350 (6.36%) | |
| Unpaid family labour | 50 (0.27%) | 15 (0.27%) | |
| Volunteer worker | 30 (0.16%) | 9 (0.16%) | |
| ‘Missing’ | 10852 (58.7%) | 3506 (63.7%) | |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | 0.547 | ||
| Mayor o igual a 90 días | 13375 (72.4%) | 4008 (72.8%) | |
| Menos de 90 días | 5100 (27.6%) | 1496 (27.2%) | |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | 0.134 | ||
| Without psychiatric comorbidity | 7191 (38.9%) | 2068 (37.6%) | |
| Diagnosis unknown (under study) | 4006 (21.7%) | 1192 (21.7%) | |
| With psychiatric comorbidity | 7278 (39.4%) | 2244 (40.8%) | |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 146 [84.0;254] | 148 [85.0;254] | 0.399 |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.949 |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | <0.001 |
| Type of Plan (Independently of the Program): | <0.001 | ||
| PAB | 7376 (39.9%) | 1901 (34.5%) | |
| PAI | 8901 (48.2%) | 2489 (45.2%) | |
| PR | 2184 (11.8%) | 1102 (20.0%) | |
| ‘Missing’ | 14 (0.08%) | 12 (0.22%) | |
| Tenure status of households: | 0.056 | ||
| Illegal Settlement | 145 (0.78%) | 46 (0.84%) | |
| Others | 424 (2.29%) | 138 (2.51%) | |
| Owner/Transferred dwellings/Pays Dividends | 5782 (31.3%) | 1720 (31.2%) | |
| Renting | 2999 (16.2%) | 873 (15.9%) | |
| Stays temporarily with a relative | 8239 (44.6%) | 2407 (43.7%) | |
| ‘Missing’ | 886 (4.80%) | 320 (5.81%) | |
| Cum. Days of Treatment (1st Treatment) | 146 [84.0;254] | 148 [85.0;255] | 0.380 |
| Cum. Days of Treatment (2nd Treatment) | . [.;.] | 318 [207;489] | . |
| Cum. Days of Treatment (3rd Treatment) | . [.;.] | 485 [335;704] | . |
| Cum. Days of Treatment (4th Treatment) | . [.;.] | 638 [433;897] | . |
| Cum. Days of Treatment (5th Treatment) | . [.;.] | 805 [550;1041] | . |
| Cum. Days of Treatment (6th Treatment) | . [.;.] | 944 [716;1152] | . |
| Cum. Days of Treatment (7th Treatment) | . [.;.] | 1076 [888;1279] | . |
| Cum. Days of Treatment (8th Treatment) | . [.;.] | 1192 [1152;1232] | . |
| Cum. Days of Treatment (9th Treatment) | . [.;.] | 1403 [1403;1403] | . |
| Cum. Days of Treatment (10th Treatment) | . [.;.] | 1622 [1622;1622] | . |
| Days of Treatment (1st Treatment) | 146 [84.0;254] | 148 [85.0;254] | 0.399 |
| Days of Treatment (2nd Treatment) | . [.;.] | 137 [77.0;239] | . |
| Days of Treatment (3rd Treatment) | . [.;.] | 134 [73.0;237] | . |
| Days of Treatment (4th Treatment) | . [.;.] | 126 [70.0;224] | . |
| Days of Treatment (5th Treatment) | . [.;.] | 143 [67.0;260] | . |
| Days of Treatment (6th Treatment) | . [.;.] | 145 [76.8;199] | . |
| Days of Treatment (7th Treatment) | . [.;.] | 120 [23.0;175] | . |
| Days of Treatment (8th Treatment) | . [.;.] | 40.0 [29.5;84.5] | . |
| Days of Treatment (9th Treatment) | . [.;.] | 211 [211;211] | . |
| Days of Treatment (10th Treatment) | . [.;.] | 219 [219;219] | . |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] | 407 [149;926] | 0.000 |
| Cum. Diff Between Treatments (2nd Treatment) | . [.;.] | 801 [405;1390] | . |
| Cum. Diff Between Treatments (3rd Treatment) | . [.;.] | 1155 [686;1680] | . |
| Cum. Diff Between Treatments (4th Treatment) | . [.;.] | 1341 [886;1972] | . |
| Cum. Diff Between Treatments (5th Treatment) | . [.;.] | 1458 [1047;1970] | . |
| Cum. Diff Between Treatments (6th Treatment) | . [.;.] | 1509 [1160;2294] | . |
| Cum. Diff Between Treatments (7th Treatment) | . [.;.] | 1188 [1184;1422] | . |
| Cum. Diff Between Treatments (8th Treatment) | . [.;.] | 1706 [1706;1706] | . |
| Cum. Diff Between Treatments (9th Treatment) | . [.;.] | 1944 [1944;1944] | . |
| Cum. Diff Between Treatments (10th Treatment) | . | . | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | . | ||
| 1 | 18472 (100.0%) | 0 (0.00%) | |
| 2 | 3 (0.02%) | 3905 (70.9%) | |
| 3 | 0 (0.00%) | 1087 (19.7%) | |
| 4 | 0 (0.00%) | 347 (6.30%) | |
| 5 | 0 (0.00%) | 111 (2.02%) | |
| 6 | 0 (0.00%) | 37 (0.67%) | |
| 7 | 0 (0.00%) | 14 (0.25%) | |
| 8 | 0 (0.00%) | 2 (0.04%) | |
| 10 | 0 (0.00%) | 1 (0.02%) | |
| Max. Cumulative Days of Treatment | 146 [84.0;254] | 380 [239;583] | 0.000 |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] | 630 [232;1268] | 0.000 |
| Total count of Psychiatric & Drug dependence Diagnostics: | <0.001 | ||
| 0 | 3463 (18.7%) | 801 (14.6%) | |
| 1 | 9719 (52.6%) | 2978 (54.1%) | |
| 2 | 5036 (27.3%) | 1641 (29.8%) | |
| 3 | 232 (1.26%) | 77 (1.40%) | |
| 4 | 25 (0.14%) | 7 (0.13%) | |
| cnt_mod_cie_10_or: | 0.238 | ||
| 0 | 7191 (38.9%) | 2068 (37.6%) | |
| 1 | 10972 (59.4%) | 3342 (60.7%) | |
| 2 | 278 (1.50%) | 87 (1.58%) | |
| 3 | 34 (0.18%) | 7 (0.13%) | |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | 0.226 | ||
| 0 | 11197 (60.6%) | 3260 (59.2%) | |
| 1 | 6966 (37.7%) | 2150 (39.1%) | |
| 2 | 278 (1.50%) | 87 (1.58%) | |
| 3 | 34 (0.18%) | 7 (0.13%) | |
| Days of Treatment (Fourth or those that follow) | . [.;.] | 138 [78.8;227] | . |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | . [.;.] | 277 [140;485] | . |
| Cumulative Days of Treatment (Fourth or those that follow) | . [.;.] | 692 [460;936] | . |
| Cumulative Difference Between Treatments (Fifth or those that follow) | . [.;.] | 1412 [1031;1995] | . |
| Average Cumulative Days of Treatment (Fourth or those that follow) | . [.;.] | 163 [110;221] | . |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | . [.;.] | 340 [236;488] | . |
| Comorbidity ICD-10 (with amount of different diagnosis): | 0.254 | ||
| Without psychiatric comorbidity | 7191 (38.9%) | 2068 (37.6%) | |
| Diagnosis unknown (under study) | 4006 (21.7%) | 1192 (21.7%) | |
| One | 6966 (37.7%) | 2150 (39.1%) | |
| Two or more | 312 (1.69%) | 94 (1.71%) | |
| No. of treatments with 18+ at admission between 2010 and 2019: | 0.000 | ||
| 01 | 18475 (100%) | 0 (0.00%) | |
| 02 | 0 (0.00%) | 3907 (71.0%) | |
| 03 | 0 (0.00%) | 1085 (19.7%) | |
| 04 or more | 0 (0.00%) | 512 (9.30%) | |
| Sexual abuse: | <0.001 | ||
| No sexual abuse | 14413 (78.0%) | 4467 (81.2%) | |
| Sexual abuse | 203 (1.10%) | 86 (1.56%) | |
| ‘Missing’ | 3859 (20.9%) | 951 (17.3%) | |
| Domestic violence: | <0.001 | ||
| No domestic violence | 10974 (59.4%) | 3330 (60.5%) | |
| Domestic violence | 3642 (19.7%) | 1223 (22.2%) | |
| ‘Missing’ | 3859 (20.9%) | 951 (17.3%) | |
| Note. Variables of C1 dataset had to be standardized before comparison; | |||
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | |||
| Categorical variables are presented as number (%) |
Show code
library(ggplot2)
jpg_path<-rstudioapi::getSourceEditorContext()$path
if (grepl("CISS Fondecyt",jpg_path)==T){
jpg_path<-paste0("C:/Users/CISS Fondecyt/Mi unidad/Alvacast/SISTRAT 2019 (github)/")
} else if (grepl("andre",jpg_path)==T){
jpg_path<-paste0('C:/Users/andre/Desktop/SUD_CL/')
} else if (grepl("E:",jpg_path)==T){
jpg_path<-paste0("E:/Mi unidad/Alvacast/SISTRAT 2019 (github)/")
} else {
jpg_path<-paste0("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/")
}
prueba2 %>%
# dplyr::group_by(hash_key)%>%
# dplyr::mutate(n_hash=n())%>%
#dplyr::filter(n_hash>1)%>%
# slice(1) %>%
# ungroup() %>%
dplyr::select(dias_treat_imp_sin_na_1,dias_treat_imp_sin_na_2,dias_treat_imp_sin_na_3,dias_treat_imp_sin_na_four) %>% #mean_cum_dias_trat_sin_na_1
tidyr::gather(option,value) %>%
dplyr::mutate(option=dplyr::case_when(option=="dias_treat_imp_sin_na_1"~"01",
option=="dias_treat_imp_sin_na_2"~"02",
option=="dias_treat_imp_sin_na_3"~"03",
option=="dias_treat_imp_sin_na_four"~"04 or more")) %>%
dplyr::mutate(option=factor(option)) %>%
dplyr::group_by(option) %>%
dplyr::mutate(count = length(na.omit(value))) %>%
dplyr::ungroup() %>%
ggplot(aes(x = option, y=value,group= option)) +
stat_summary(fun = mean, geom="bar",alpha=.8, na.rm = T)+
stat_summary(fun = median, geom="point", na.rm = T)+
stat_summary(fun = median,
fun.min = function(x) quantile(x,.25, na.rm = T),
fun.max = function(x) quantile(x,.75, na.rm = T),
geom = "errorbar", width = 0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option) %>% slice(1),
aes(label = paste0(count, " Obs."), x = option), y = -0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option) %>% slice(1),
aes(label = paste0(count, " Obs."), x = option), y = -0.5)+
theme_bw()+
theme(plot.caption = element_text(hjust = 0, face= "italic",size=9))+
#geom_bar(stat = "identity")+
#geom_errorbar() +
labs(x="Number of Treatment of Each User", y="Days in Treatment",caption=paste0("Note. Bars=Means, Dots= Medians, Error bars= Percentiles 25 and 75"))
Figure 1: Figura 1. Días de Tratamiento, según la posición del tratamiento en cada usuario usuario
Show code
library(ggplot2)
prueba2 %>%
# dplyr::group_by(hash_key)%>%
# dplyr::mutate(n_hash=n())%>%
#dplyr::filter(n_hash>1)%>%
# slice(1) %>%
# ungroup() %>%
dplyr::select(diff_bet_treat_1,diff_bet_treat_2,diff_bet_treat_3,diff_bet_treat_four) %>% #mean_cum_dias_trat_sin_na_1
tidyr::gather(option,value) %>%
dplyr::mutate(option=dplyr::case_when(option=="diff_bet_treat_1"~"01",
option=="diff_bet_treat_2"~"02",
option=="diff_bet_treat_3"~"03",
option=="diff_bet_treat_four"~"04 or more")) %>%
dplyr::mutate(option=factor(option)) %>%
dplyr::group_by(option) %>%
dplyr::mutate(count = length(na.omit(value))) %>%
dplyr::ungroup() %>%
ggplot(aes(x = option, y=value,group= option)) +
stat_summary(fun = mean, geom="bar",alpha=.8, na.rm = T)+
stat_summary(fun = median, geom="point", na.rm = T)+
stat_summary(fun = median,
fun.min = function(x) quantile(x,.25, na.rm = T),
fun.max = function(x) quantile(x,.75, na.rm = T),
geom = "errorbar", width = 0.5)+
#geom_label(inherit.aes = F,aes(x = option, y=value, label = max(count, na.rm=T)), vjust = -0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option) %>% slice(1),
aes(label = paste0(count, " Obs."), x = option), y = -0.5)+
theme_bw()+
theme(plot.caption = element_text(hjust = 0, face= "italic",size=9))+
#geom_bar(stat = "identity")+
#geom_errorbar() +
labs(x="Number of Treatment of Each User", y="Difference (in days) with the posterior treatment",caption=paste0("Note. Bars=Means, Dots= Medians, Error bars= Percentiles 25 and 75"))
Figure 2: Figura 2. Diferencia en Días con el Tratamiento Siguiente, según la posición del tratamiento en cada usuario usuario
Show code
library(ggplot2)
f3<-
prueba2 %>%
# dplyr::group_by(hash_key)%>%
# dplyr::mutate(n_hash=n())%>%
#dplyr::filter(n_hash>1)%>%
# slice(1) %>%
# ungroup() %>%
dplyr::select(con_quien_vive_joel,dias_treat_imp_sin_na_1,dias_treat_imp_sin_na_2,dias_treat_imp_sin_na_3,dias_treat_imp_sin_na_four) %>% #mean_cum_dias_trat_sin_na_1con_quien_vive_joel
tidyr::gather(option,value,-con_quien_vive_joel) %>%
dplyr::mutate(option=dplyr::case_when(option=="dias_treat_imp_sin_na_1"~"01",
option=="dias_treat_imp_sin_na_2"~"02",
option=="dias_treat_imp_sin_na_3"~"03",
option=="dias_treat_imp_sin_na_four"~"04 or more")) %>%
dplyr::mutate(option=factor(option)) %>%
dplyr::group_by(con_quien_vive_joel,option) %>%
dplyr::mutate(count = length(na.omit(value))) %>%
dplyr::ungroup() %>%
ggplot(aes(x = option, y=value,group= option)) +
stat_summary(fun = mean, geom="bar",alpha=.8, na.rm = T)+
stat_summary(fun = median, geom="point", na.rm = T)+
stat_summary(fun = median,
fun.min = function(x) quantile(x,.25, na.rm = T),
fun.max = function(x) quantile(x,.75, na.rm = T),
geom = "errorbar", width = 0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option,con_quien_vive_joel) %>% slice(1),
aes(label = paste0("n=",count), x = option), y = -0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option,con_quien_vive_joel) %>% slice(1),
aes(label = paste0("n=",count), x = option), y = -0.5)+
facet_wrap(~con_quien_vive_joel)+
theme_bw()+
theme(plot.caption = element_text(hjust = 0, face= "italic",size=9))+
#geom_bar(stat = "identity")+
#geom_errorbar() +
labs(x="Number of Treatment of Each User", y="Days in Treatment",caption=paste0("Note. Bars=Means, Dots= Medians, Error bars= Percentiles 25 and 75"))
f3
Figure 3: Figura 3. Días de Tratamiento, según la posición del tratamiento en cada usuario usuario
Imputation (October 2021)
We generated a plot to see all the missing values in the sample.
Show code
#<div style="border: 1px solid #ddd; padding: 5px; overflow-y: scroll; height:400px; overflow-x: scroll; width:100%">
library(dplyr)
library(ggplot2)
#
# sexo_2=3,
# escolaridad_rec=3, 76 (0.32%)
# estado_conyugal_2=3, 27 (0.11%)
# edad_ini_sus_prin=2, 2755 (11,5%)
# edad_al_ing=2,
# sus_ini_mod=3, 2074 (8.65%)
# sus_ini_mod_mvv=3, 2074 (8.65%)
# freq_cons_sus_prin=3, 122 (0.51%)
# via_adm_sus_prin_act=3, 4 (0.02%)
# con_quien_vive_joel=3,
# numero_de_hijos_mod_joel=2, 293 (1,2%)
# condicion_ocupacional_corr=3, 14358 (59.9%)
# cat_ocupacional_corr=3, 14358 (59.9%)
# abandono_temprano=3,
# dg_cie_10_rec=3,
# dias_treat_imp_sin_na=2,
# cnt_mod_cie_10_or=3,
# cnt_diagnostico_trs_fisico=2,
# cnt_otros_probl_at_sm_or=2,
# tipo_de_plan_2_mod=3, 26 (0.11%)
# cnt_mod_cie_10_dg_cons_sus_or= 3,
# dg_total_cie_10 = 3,
# comorbidity_icd_10 = 3,
# compromiso_biopsicosocial 438 (1.83%)
# tenencia_de_la_vivienda_mod 1206 (5.03%)
vector_variables<-
c("sexo_2", "escolaridad_rec", "estado_conyugal_2", "edad_ini_cons", "sus_ini_mod_mvv", "freq_cons_sus_prin", "condicion_ocupacional_corr", "via_adm_sus_prin_act", "con_quien_vive_joel", "numero_de_hijos_mod_joel", "condicion_ocupacional_corr", "dias_treat_imp_sin_na", "cnt_mod_cie_10_or", "tipo_de_plan_2_mod", "comorbidity_icd_10", "compromiso_biopsicosocial", "tenencia_de_la_vivienda_mod","tipo_centro","macrozona","compromiso_biopsicosocial")
missing.values<-
prueba2 %>%
rowwise %>%
dplyr::mutate_at(.vars = vars(vector_variables),
.funs = ~ifelse(is.na(.), 1, 0)) %>%
dplyr::ungroup() %>%
dplyr::summarise_at(vars(vector_variables),~sum(.))
#t(missing.values)
miss_val_bar<-
data.table::melt(missing.values) %>%
mutate(perc=scales::percent(value/nrow(prueba2))) %>%
arrange(desc(perc))
plot_miss<-
missing.values %>%
data.table::melt() %>% #condicion_ocupacional_corr
dplyr::filter(!variable %in% c("row", "hash_key", "dias_treat_imp_sin_na", "dup")) %>%
dplyr::mutate(perc= value/sum(nrow(prueba2))) %>%
dplyr::mutate(label_text= paste0("Variable= ",variable,"<br>n= ",value,"<br>",scales::percent(round(perc,3)))) %>%
dplyr::mutate(perc=perc*100) %>%
ggplot() +
geom_bar(aes(x=factor(variable), y=perc,label= label_text), stat = 'identity') +
theme_classic()+
# scale_y_continuous(limits=c(0,1), labels=percent)+
theme(axis.text.x = element_text(angle = 90, hjust = 1, size=9))+
labs(x=NULL, y="% of Missing Values", caption=paste0("Nota. Percentage of missing values (n= ",format(nrow(prueba2),big.mark=","),")"))
ggplotly(plot_miss, tooltip = c("label_text"))%>% layout(xaxis= list(showticklabels = T), height = 600, width=800) %>% layout(yaxis = list(tickformat='%', range = c(0, 15)))Figure 4: Bar plot of Percentage of Missing Values per Variables at Basline
Show code
#</div>From the figure above, we could see that the Age of Onset of Drug Use (edad_ini_cons), Starting substance (sus_ini_mod_mvv), the Substance Tenure status of households (tenencia_de_la_vivienda_mod) and the Biopsychosocial Involvement (compromiso_biopsicosocial) had a proportion of missing values, but no greater than 5%. This is why we imputed these values under MAR assumption.
Show code
vector_variables_only_for_imputation<-
c("row","hash_key", "edad_al_ing", "sexo_2", "escolaridad_rec", "estado_conyugal_2", "edad_ini_cons", "sus_ini_mod_mvv", "freq_cons_sus_prin", "condicion_ocupacional_corr", "via_adm_sus_prin_act", "con_quien_vive_joel", "numero_de_hijos_mod_joel", "condicion_ocupacional_corr", "dias_treat_imp_sin_na", "tipo_de_plan_2_mod", "comorbidity_icd_10", "compromiso_biopsicosocial", "tenencia_de_la_vivienda_mod", "tipo_centro", "macrozona", "compromiso_biopsicosocial", "sus_ini_mod")#cnt_mod_cie_10_or",
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
prueba2_miss<-
prueba2 %>%
#dplyr::group_by(hash_key) %>%
#dplyr::mutate(rn=row_number()) %>%
#dplyr::ungroup() %>%
#:#:#:#:#:#:#:#:#:#:#:
# ORDINALIZAR LAS VARIABLES ORDINALES:
dplyr::select_(.dots = vector_variables_only_for_imputation) %>%
dplyr::mutate(numero_de_hijos_mod_joel=dplyr::case_when(numero_de_hijos_mod_joel>3~"4 or more",
T~as.character(numero_de_hijos_mod_joel))) %>%
#dplyr::select(-hash_key) %>%
as.data.frame()
#CONS_C1_df_dup_SEP_2020 %>% janitor::tabyl(evaluacindelprocesoteraputico)
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
time_before_missforest<-Sys.time()
# for (var in colnames(prueba2_miss)) {
# attr(prueba2_miss[[deparse(as.name(var))]], "label") <- NULL
# }
library(missRanger)
set.seed(2125)
prueba2_imp <- missRanger(
prueba2_miss,
formula = . ~ . - row - hash_key,
num.trees = 200,
returnOOB=T,
maxiter=50,
verbose = 2,
seed = 2125)
time_after_missforest<-Sys.time()
paste0("Time in imputation process: ");time_after_missforest-time_before_missforestShow code
ggplotly(DataExplorer::plot_missing(prueba2,missing_only = T))Descriptives
Show code
attr(prueba2_imp$numero_de_hijos_mod_joel,"label") <-"Number of Children (Max. Value), adding 1 if pregnant at admission"
attr(prueba2$cnt_mod_cie_10_or,"label") <- "Total count of Psychiatric Diagnostics"
attr(prueba2_imp$escolaridad_rec,"label") <- "Educational Attainment"
table6_imp <- compareGroups::compareGroups(no_group ~ sexo_2+ escolaridad_rec+ estado_conyugal_2+ compromiso_biopsicosocial+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+ cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+ dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(sexo_2=3,
escolaridad_rec=3,
estado_conyugal_2=3,
compromiso_biopsicosocial=2,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered= 3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 2,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = cbind.data.frame(prueba2_imp,
dplyr::select(prueba2,cum_dias_trat_sin_na_1, cum_dias_trat_sin_na_2, cum_dias_trat_sin_na_3, cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10, dias_treat_imp_sin_na_1, dias_treat_imp_sin_na_2, dias_treat_imp_sin_na_3, dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10, cum_diff_bet_treat_1, cum_diff_bet_treat_2, cum_diff_bet_treat_3, cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10),
#
dplyr::select(prueba2,sus_principal_mod, cat_ocupacional_corr, cnt_diagnostico_trs_fisico, cnt_otros_probl_at_sm_or, dg_cie_10_rec, abandono_temprano, duplicates_filtered, max_cum_dias_trat_sin_na, max_cum_diff_bet_treat, cnt_mod_cie_10_dg_cons_sus_or, cnt_mod_cie_10_or, dg_total_cie_10, dias_treat_imp_sin_na_four, diff_bet_treat_four, cum_dias_trat_sin_na_four, cum_diff_bet_treat_four, mean_cum_dias_trat_sin_na_four, mean_cum_diff_bet_treat_four, n_treats, sex_abuse, dom_violence))%>% dplyr::mutate(no_group=1),
include.miss = T,
var.equal=T
)
pvals <- getResults(table6_imp)
restab6_imp <- createTable(table6_imp,show.p.overall = F)
compareGroups::export2md(restab6_imp, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Total"),
format="html",caption= "Summary descriptives")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")| Variables | Total |
|---|---|
| N=23979 | |
| Sexo Usuario/Sex of User: | |
| Men | 19850 (82.8%) |
| Women | 4129 (17.2%) |
| Educational Attainment: | |
| 3-Completed primary school or less | 5638 (23.5%) |
| 2-Completed high school or less | 14586 (60.8%) |
| 1-More than high school | 3755 (15.7%) |
| Estado Conyugal/Marital Status: | |
| Married/Shared living arrangements | 5137 (21.4%) |
| Separated/Divorced | 501 (2.09%) |
| Single | 18311 (76.4%) |
| Widower | 30 (0.13%) |
| Biopsychosocial Compromise: | |
| 1-Mild | 2245 (9.36%) |
| 2-Moderate | 14309 (59.7%) |
| 3-Severe | 7425 (31.0%) |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 14.9 [13.0;16.0] |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 25.4 [22.6;27.7] |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | |
| Alcohol | 11212 (46.8%) |
| Cocaine hydrochloride | 936 (3.90%) |
| Cocaine paste | 1058 (4.41%) |
| Marijuana | 10362 (43.2%) |
| Other | 411 (1.71%) |
| Starting Substance: | |
| Alcohol | 11053 (46.1%) |
| Cocaine hydrochloride | 972 (4.05%) |
| Marijuana | 10452 (43.6%) |
| Other | 398 (1.66%) |
| Cocaine paste | 1104 (4.60%) |
| freq_cons_sus_prin: | |
| 1 day a week or more | 1616 (6.74%) |
| 2 to 3 days a week | 7128 (29.7%) |
| 4 to 6 days a week | 4108 (17.1%) |
| Daily | 9961 (41.5%) |
| Less than 1 day a week | 1166 (4.86%) |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | |
| Smoked or Pulmonary Aspiration | 13906 (58.0%) |
| Intranasal (powder aspiration) | 5575 (23.2%) |
| Injected Intravenously or Intramuscularly | 16 (0.07%) |
| Oral (drunk or eaten) | 4470 (18.6%) |
| Other | 12 (0.05%) |
| Whom you live with(cohabitation status) (Recoded) (f): | |
| Alone | 1335 (5.57%) |
| Family of origin | 15455 (64.5%) |
| With couple/children | 7189 (30.0%) |
| Number of Children (Max. Value), adding 1 if pregnant at admission: | |
| 0 | 9730 (40.6%) |
| 1 | 8664 (36.1%) |
| 2 | 3929 (16.4%) |
| 3 | 1218 (5.08%) |
| 4 or more | 438 (1.83%) |
| Occupational Status Corrected(f): | |
| Employed | 10423 (43.5%) |
| Inactive | 2019 (8.42%) |
| Looking for a job for the first time | 97 (0.40%) |
| No activity | 1333 (5.56%) |
| Not seeking for work | 216 (0.90%) |
| Unemployed | 9891 (41.2%) |
| Occupational Category Corrected(f): | |
| Employer | 322 (1.34%) |
| Other | 222 (0.93%) |
| Salaried | 7039 (29.4%) |
| Self-employed | 1934 (8.07%) |
| Unpaid family labour | 65 (0.27%) |
| Volunteer worker | 39 (0.16%) |
| ‘Missing’ | 14358 (59.9%) |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | |
| Mayor o igual a 90 días | 17383 (72.5%) |
| Menos de 90 días | 6596 (27.5%) |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | |
| Without psychiatric comorbidity | 9259 (38.6%) |
| Diagnosis unknown (under study) | 5198 (21.7%) |
| With psychiatric comorbidity | 9522 (39.7%) |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 147 [84.0;254] |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] |
| Type of Plan (Independently of the Program): | |
| PAB | 9282 (38.7%) |
| PAI | 11407 (47.6%) |
| PR | 3290 (13.7%) |
| Tenure status of households: | |
| Illegal Settlement | 235 (0.98%) |
| Others | 566 (2.36%) |
| Owner/Transferred dwellings/Pays Dividends | 7759 (32.4%) |
| Renting | 4055 (16.9%) |
| Stays temporarily with a relative | 11364 (47.4%) |
| Cum. Days of Treatment (1st Treatment) | 147 [84.0;254] |
| Cum. Days of Treatment (2nd Treatment) | 318 [207;489] |
| Cum. Days of Treatment (3rd Treatment) | 485 [335;704] |
| Cum. Days of Treatment (4th Treatment) | 638 [433;897] |
| Cum. Days of Treatment (5th Treatment) | 805 [550;1041] |
| Cum. Days of Treatment (6th Treatment) | 944 [716;1152] |
| Cum. Days of Treatment (7th Treatment) | 1076 [888;1279] |
| Cum. Days of Treatment (8th Treatment) | 1192 [1152;1232] |
| Cum. Days of Treatment (9th Treatment) | 1403 [1403;1403] |
| Cum. Days of Treatment (10th Treatment) | 1622 [1622;1622] |
| Days of Treatment (1st Treatment) | 147 [84.0;254] |
| Days of Treatment (2nd Treatment) | 137 [77.0;239] |
| Days of Treatment (3rd Treatment) | 134 [73.0;237] |
| Days of Treatment (4th Treatment) | 126 [70.0;224] |
| Days of Treatment (5th Treatment) | 143 [67.0;260] |
| Days of Treatment (6th Treatment) | 145 [76.8;199] |
| Days of Treatment (7th Treatment) | 120 [23.0;175] |
| Days of Treatment (8th Treatment) | 40.0 [29.5;84.5] |
| Days of Treatment (9th Treatment) | 211 [211;211] |
| Days of Treatment (10th Treatment) | 219 [219;219] |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] |
| Cum. Diff Between Treatments (2nd Treatment) | 801 [405;1390] |
| Cum. Diff Between Treatments (3rd Treatment) | 1155 [686;1680] |
| Cum. Diff Between Treatments (4th Treatment) | 1341 [886;1972] |
| Cum. Diff Between Treatments (5th Treatment) | 1458 [1047;1970] |
| Cum. Diff Between Treatments (6th Treatment) | 1509 [1160;2294] |
| Cum. Diff Between Treatments (7th Treatment) | 1188 [1184;1422] |
| Cum. Diff Between Treatments (8th Treatment) | 1706 [1706;1706] |
| Cum. Diff Between Treatments (9th Treatment) | 1944 [1944;1944] |
| Cum. Diff Between Treatments (10th Treatment) | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | |
| 1 | 18472 (77.0%) |
| 2 | 3908 (16.3%) |
| 3 | 1087 (4.53%) |
| 4 | 347 (1.45%) |
| 5 | 111 (0.46%) |
| 6 | 37 (0.15%) |
| 7 | 14 (0.06%) |
| 8 | 2 (0.01%) |
| 10 | 1 (0.00%) |
| Max. Cumulative Days of Treatment | 182 [98.0;332] |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] |
| Total count of Psychiatric & Drug dependence Diagnostics | 1.00 [1.00;2.00] |
| Total count of Psychiatric Diagnostics: | |
| 0 | 9259 (38.6%) |
| 1 | 14314 (59.7%) |
| 2 | 365 (1.52%) |
| 3 | 41 (0.17%) |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | |
| 0 | 14457 (60.3%) |
| 1 | 9116 (38.0%) |
| 2 | 365 (1.52%) |
| 3 | 41 (0.17%) |
| Days of Treatment (Fourth or those that follow) | 138 [78.8;227] |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | 277 [140;485] |
| Cumulative Days of Treatment (Fourth or those that follow) | 692 [460;936] |
| Cumulative Difference Between Treatments (Fifth or those that follow) | 1412 [1031;1995] |
| Average Cumulative Days of Treatment (Fourth or those that follow) | 163 [110;221] |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | 340 [236;488] |
| Comorbidity ICD-10 (with amount of different diagnosis): | |
| Without psychiatric comorbidity | 9259 (38.6%) |
| Diagnosis unknown (under study) | 5198 (21.7%) |
| One | 9116 (38.0%) |
| Two or more | 406 (1.69%) |
| No. of treatments with 18+ at admission between 2010 and 2019: | |
| 01 | 18475 (77.0%) |
| 02 | 3907 (16.3%) |
| 03 | 1085 (4.52%) |
| 04 or more | 512 (2.14%) |
| Sexual abuse: | |
| No sexual abuse | 18880 (78.7%) |
| Sexual abuse | 289 (1.21%) |
| ‘Missing’ | 4810 (20.1%) |
| Domestic violence: | |
| No domestic violence | 14304 (59.7%) |
| Domestic violence | 4865 (20.3%) |
| ‘Missing’ | 4810 (20.1%) |
| Note. Variables of C1 dataset had to be standardized before comparison; | |
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | |
| Categorical variables are presented as number (%) |
Living with
Show code
table5_imp <- compareGroups::compareGroups(con_quien_vive_joel ~ sus_principal_mod+ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ numero_de_hijos_mod_joel+ numero_de_hijos_mod_joel_bin+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(sus_principal_mod=3,
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
numero_de_hijos_mod_joel=2,
numero_de_hijos_mod_joel_bin=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = cbind.data.frame(prueba2_imp,
dplyr::select(prueba2,cum_dias_trat_sin_na_1, cum_dias_trat_sin_na_2, cum_dias_trat_sin_na_3, cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10, dias_treat_imp_sin_na_1, dias_treat_imp_sin_na_2, dias_treat_imp_sin_na_3, dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10, cum_diff_bet_treat_1, cum_diff_bet_treat_2, cum_diff_bet_treat_3, cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10),
#
dplyr::select(prueba2,sus_principal_mod, cat_ocupacional_corr, cnt_diagnostico_trs_fisico, cnt_otros_probl_at_sm_or, dg_cie_10_rec, abandono_temprano, duplicates_filtered, max_cum_dias_trat_sin_na, max_cum_diff_bet_treat, cnt_mod_cie_10_dg_cons_sus_or, cnt_mod_cie_10_or, dg_total_cie_10, dias_treat_imp_sin_na_four, diff_bet_treat_four, cum_dias_trat_sin_na_four, cum_diff_bet_treat_four, mean_cum_dias_trat_sin_na_four, mean_cum_diff_bet_treat_four, n_treats, sex_abuse, dom_violence)) %>%
dplyr::mutate(numero_de_hijos_mod_joel_bin=dplyr::case_when(numero_de_hijos_mod_joel=="0"~"0",
!is.na(numero_de_hijos_mod_joel)~"One or more (or pregnant)")),
include.miss = T,
var.equal=T,
max.xlev = 10,
max.ylev = 10
)#cie_10 cat_ocupacional estatus_ocupacional
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
if(no_mostrar==1){
catVars_after_imp<-
cbind.data.frame(prueba2_imp,
dplyr::select(prueba2,cum_dias_trat_sin_na_1, cum_dias_trat_sin_na_2, cum_dias_trat_sin_na_3, cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10, dias_treat_imp_sin_na_1, dias_treat_imp_sin_na_2, dias_treat_imp_sin_na_3, dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10, cum_diff_bet_treat_1, cum_diff_bet_treat_2, cum_diff_bet_treat_3, cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10),
#
dplyr::select(prueba2,sus_principal_mod, cat_ocupacional_corr, cnt_diagnostico_trs_fisico, cnt_otros_probl_at_sm_or, dg_cie_10_rec, abandono_temprano, duplicates_filtered, max_cum_dias_trat_sin_na, max_cum_diff_bet_treat, cnt_mod_cie_10_dg_cons_sus_or, cnt_mod_cie_10_or, dg_total_cie_10, dias_treat_imp_sin_na_four, diff_bet_treat_four, cum_dias_trat_sin_na_four, cum_diff_bet_treat_four, mean_cum_dias_trat_sin_na_four, mean_cum_diff_bet_treat_four, n_treats, sex_abuse, dom_violence)) %>%
dplyr::mutate(numero_de_hijos_mod_joel_bin=dplyr::case_when(numero_de_hijos_mod_joel=="0"~"0",
!is.na(numero_de_hijos_mod_joel)~"One or more (or pregnant)")) %>%
dplyr::select_if(~!is.numeric(.x)) %>% names()
#prueba
cbind.data.frame(prueba2_imp,
dplyr::select(prueba2,cum_dias_trat_sin_na_1, cum_dias_trat_sin_na_2, cum_dias_trat_sin_na_3, cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10, dias_treat_imp_sin_na_1, dias_treat_imp_sin_na_2, dias_treat_imp_sin_na_3, dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10, cum_diff_bet_treat_1, cum_diff_bet_treat_2, cum_diff_bet_treat_3, cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10),
#
dplyr::select(prueba2,sus_principal_mod, cat_ocupacional_corr, cnt_diagnostico_trs_fisico, cnt_otros_probl_at_sm_or, dg_cie_10_rec, abandono_temprano, duplicates_filtered, max_cum_dias_trat_sin_na, max_cum_diff_bet_treat, cnt_mod_cie_10_dg_cons_sus_or, cnt_mod_cie_10_or, dg_total_cie_10, dias_treat_imp_sin_na_four, diff_bet_treat_four, cum_dias_trat_sin_na_four, cum_diff_bet_treat_four, mean_cum_dias_trat_sin_na_four, mean_cum_diff_bet_treat_four, n_treats, sex_abuse, dom_violence)) %>%
dplyr::mutate(numero_de_hijos_mod_joel_bin=dplyr::case_when(numero_de_hijos_mod_joel=="0"~"0",
!is.na(numero_de_hijos_mod_joel)~"One or more (or pregnant)")) %>%
dplyr::select(catVars_after_imp) %>%
tidyr::gather(variable,measure, -con_quien_vive_joel) %>%
group_by(variable) %>%
do(chisq.test(.$con_quien_vive_joel, .$measure) %>% broom::tidy()) %>%
dplyr::mutate(p.value=ifelse(p.value<.001,"<0.001",sprintf("%1.3f",p.value))) %>%
dplyr::mutate(statistic=sprintf("%2.2f",statistic)) %>%
dplyr::mutate(report=paste0("X²(",parameter,", N=",nrow(prueba2),")=",statistic,ifelse(p.value=="<0.001",p.value, paste0("=",p.value)))) %>% copiar_nombres()
}
if(no_mostrar==1){
invisible("Prueba post-revisión para sacar el wilcox y test de asociaciones")
broom::tidy(wilcox.test(dias_treat_imp_sin_na ~ tipo_de_programa_2, data=CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados)) %>% dplyr::mutate(report=paste0("W"))
#effect size
broom::tidy(wilcox.test(dias_treat_imp_sin_na ~ tipo_de_programa_2, data=CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados))$statistic/sqrt(nrow(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados))
broom::tidy(t.test(dias_treat_imp_sin_na ~ tipo_de_programa_2, data=CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados)) %>%
dplyr::mutate(report=paste0("t(",round(parameter,0),")=",sprintf("%1.2f",statistic),",",ifelse(p.value<0.001,"<0.001", paste0("=",sprinf("f1.3f",p.value)))))
# t(19) = 3.1, p = .006.
}
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
pvals <- getResults(table5_imp)
#p.adjust(pvals, method = "BH")
restab5_imp <- createTable(table5_imp, show.p.overall = T)
compareGroups::export2md(restab5_imp, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Alone","Family of origin", "With couple", "P-value"),
format="html",caption= "Summary descriptives by With whom they live (with some imputed variables")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")| Variables | Alone | Family of origin | With couple | P-value |
|---|---|---|---|---|
| N=1335 | N=15455 | N=7189 | ||
| Sustancia Principal de Consumo (Sólo más frecuentes)(f)/Primary or Main Substance of Consumption at Admission (Only more frequent)(f): | <0.001 | |||
| Alcohol | 311 (23.3%) | 2434 (15.7%) | 1397 (19.4%) | |
| Cocaine hydrochloride | 199 (14.9%) | 3384 (21.9%) | 1811 (25.2%) | |
| Marijuana | 148 (11.1%) | 2031 (13.1%) | 693 (9.64%) | |
| Other | 16 (1.20%) | 222 (1.44%) | 57 (0.79%) | |
| Cocaine paste | 661 (49.5%) | 7384 (47.8%) | 3231 (44.9%) | |
| Sexo Usuario/Sex of User: | <0.001 | |||
| Men | 1171 (87.7%) | 13154 (85.1%) | 5525 (76.9%) | |
| Women | 164 (12.3%) | 2301 (14.9%) | 1664 (23.1%) | |
| Educational Attainment: | <0.001 | |||
| 3-Completed primary school or less | 359 (26.9%) | 3336 (21.6%) | 1943 (27.0%) | |
| 2-Completed high school or less | 774 (58.0%) | 9456 (61.2%) | 4356 (60.6%) | |
| 1-More than high school | 202 (15.1%) | 2663 (17.2%) | 890 (12.4%) | |
| Biopsychosocial Compromise: | <0.001 | |||
| 1-Mild | 92 (6.89%) | 1332 (8.62%) | 821 (11.4%) | |
| 2-Moderate | 649 (48.6%) | 9080 (58.8%) | 4580 (63.7%) | |
| 3-Severe | 594 (44.5%) | 5043 (32.6%) | 1788 (24.9%) | |
| Estado Conyugal/Marital Status: | . | |||
| Married/Shared living arrangements | 101 (7.57%) | 858 (5.55%) | 4178 (58.1%) | |
| Separated/Divorced | 55 (4.12%) | 361 (2.34%) | 85 (1.18%) | |
| Single | 1178 (88.2%) | 14212 (92.0%) | 2921 (40.6%) | |
| Widower | 1 (0.07%) | 24 (0.16%) | 5 (0.07%) | |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 14.0 [13.0;16.0] | 15.0 [13.0;16.0] | 14.9 [13.0;16.0] | <0.001 |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 26.3 [23.5;28.3] | 24.7 [22.0;27.3] | 26.4 [24.1;28.3] | <0.001 |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | <0.001 | |||
| Alcohol | 634 (47.5%) | 7046 (45.6%) | 3532 (49.1%) | |
| Cocaine hydrochloride | 37 (2.77%) | 548 (3.55%) | 351 (4.88%) | |
| Cocaine paste | 68 (5.09%) | 637 (4.12%) | 353 (4.91%) | |
| Marijuana | 570 (42.7%) | 6974 (45.1%) | 2818 (39.2%) | |
| Other | 26 (1.95%) | 250 (1.62%) | 135 (1.88%) | |
| Starting Substance: | <0.001 | |||
| Alcohol | 628 (47.0%) | 6926 (44.8%) | 3499 (48.7%) | |
| Cocaine hydrochloride | 39 (2.92%) | 571 (3.69%) | 362 (5.04%) | |
| Marijuana | 573 (42.9%) | 7045 (45.6%) | 2834 (39.4%) | |
| Other | 24 (1.80%) | 245 (1.59%) | 129 (1.79%) | |
| Cocaine paste | 71 (5.32%) | 668 (4.32%) | 365 (5.08%) | |
| freq_cons_sus_prin: | <0.001 | |||
| 1 day a week or more | 55 (4.12%) | 904 (5.85%) | 657 (9.14%) | |
| 2 to 3 days a week | 331 (24.8%) | 4375 (28.3%) | 2422 (33.7%) | |
| 4 to 6 days a week | 204 (15.3%) | 2739 (17.7%) | 1165 (16.2%) | |
| Daily | 700 (52.4%) | 6804 (44.0%) | 2457 (34.2%) | |
| Less than 1 day a week | 45 (3.37%) | 633 (4.10%) | 488 (6.79%) | |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | . | |||
| Smoked or Pulmonary Aspiration | 799 (59.9%) | 9269 (60.0%) | 3838 (53.4%) | |
| Intranasal (powder aspiration) | 205 (15.4%) | 3505 (22.7%) | 1865 (25.9%) | |
| Injected Intravenously or Intramuscularly | 2 (0.15%) | 12 (0.08%) | 2 (0.03%) | |
| Oral (drunk or eaten) | 328 (24.6%) | 2662 (17.2%) | 1480 (20.6%) | |
| Other | 1 (0.07%) | 7 (0.05%) | 4 (0.06%) | |
| Number of Children (Max. Value), adding 1 if pregnant at admission: | 0.000 | |||
| 0 | 586 (43.9%) | 8149 (52.7%) | 995 (13.8%) | |
| 1 | 463 (34.7%) | 4959 (32.1%) | 3242 (45.1%) | |
| 2 | 184 (13.8%) | 1724 (11.2%) | 2021 (28.1%) | |
| 3 | 70 (5.24%) | 477 (3.09%) | 671 (9.33%) | |
| 4 or more | 32 (2.40%) | 146 (0.94%) | 260 (3.62%) | |
| numero_de_hijos_mod_joel_bin: | 0.000 | |||
| 0 | 586 (43.9%) | 8149 (52.7%) | 995 (13.8%) | |
| One or more (or pregnant) | 749 (56.1%) | 7306 (47.3%) | 6194 (86.2%) | |
| Occupational Status Corrected(f): | <0.001 | |||
| Employed | 648 (48.5%) | 5601 (36.2%) | 4174 (58.1%) | |
| Inactive | 57 (4.27%) | 1231 (7.97%) | 731 (10.2%) | |
| Looking for a job for the first time | 3 (0.22%) | 78 (0.50%) | 16 (0.22%) | |
| No activity | 86 (6.44%) | 1015 (6.57%) | 232 (3.23%) | |
| Not seeking for work | 34 (2.55%) | 154 (1.00%) | 28 (0.39%) | |
| Unemployed | 507 (38.0%) | 7376 (47.7%) | 2008 (27.9%) | |
| Occupational Category Corrected(f): | . | |||
| Employer | 14 (1.05%) | 163 (1.05%) | 145 (2.02%) | |
| Other | 17 (1.27%) | 128 (0.83%) | 77 (1.07%) | |
| Salaried | 432 (32.4%) | 3833 (24.8%) | 2774 (38.6%) | |
| Self-employed | 119 (8.91%) | 953 (6.17%) | 862 (12.0%) | |
| Unpaid family labour | 1 (0.07%) | 52 (0.34%) | 12 (0.17%) | |
| Volunteer worker | 2 (0.15%) | 25 (0.16%) | 12 (0.17%) | |
| ‘Missing’ | 750 (56.2%) | 10301 (66.7%) | 3307 (46.0%) | |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | <0.001 | |||
| Mayor o igual a 90 días | 865 (64.8%) | 11229 (72.7%) | 5289 (73.6%) | |
| Menos de 90 días | 470 (35.2%) | 4226 (27.3%) | 1900 (26.4%) | |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | <0.001 | |||
| Without psychiatric comorbidity | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| Diagnosis unknown (under study) | 346 (25.9%) | 3322 (21.5%) | 1530 (21.3%) | |
| With psychiatric comorbidity | 536 (40.1%) | 6415 (41.5%) | 2571 (35.8%) | |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 125 [68.0;223] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.015 |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | <0.001 |
| Type of Plan (Independently of the Program): | <0.001 | |||
| PAB | 391 (29.3%) | 5510 (35.7%) | 3381 (47.0%) | |
| PAI | 587 (44.0%) | 7520 (48.7%) | 3300 (45.9%) | |
| PR | 357 (26.7%) | 2425 (15.7%) | 508 (7.07%) | |
| Tenure status of households: | 0.000 | |||
| Illegal Settlement | 97 (7.27%) | 48 (0.31%) | 90 (1.25%) | |
| Others | 78 (5.84%) | 374 (2.42%) | 114 (1.59%) | |
| Owner/Transferred dwellings/Pays Dividends | 307 (23.0%) | 5496 (35.6%) | 1956 (27.2%) | |
| Renting | 680 (50.9%) | 1155 (7.47%) | 2220 (30.9%) | |
| Stays temporarily with a relative | 173 (13.0%) | 8382 (54.2%) | 2809 (39.1%) | |
| Cum. Days of Treatment (1st Treatment) | 125 [68.0;224] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Cum. Days of Treatment (2nd Treatment) | 285 [174;423] | 320 [206;493] | 326 [218;489] | <0.001 |
| Cum. Days of Treatment (3rd Treatment) | 463 [260;644] | 474 [326;700] | 522 [361;730] | 0.008 |
| Cum. Days of Treatment (4th Treatment) | 526 [339;755] | 633 [426;878] | 671 [486;941] | 0.059 |
| Cum. Days of Treatment (5th Treatment) | 690 [532;1120] | 798 [541;1047] | 876 [576;1021] | 0.604 |
| Cum. Days of Treatment (6th Treatment) | 460 [356;565] | 897 [712;1153] | 1006 [868;1144] | 0.106 |
| Cum. Days of Treatment (7th Treatment) | 477 [376;578] | 1165 [993;1331] | 1076 [1074;1254] | 0.100 |
| Cum. Days of Treatment (8th Treatment) | . [.;.] | 1152 [1131;1172] | 1273 [1273;1273] | 0.221 |
| Cum. Days of Treatment (9th Treatment) | . [.;.] | 1403 [1403;1403] | . [.;.] | . |
| Cum. Days of Treatment (10th Treatment) | . [.;.] | 1622 [1622;1622] | . [.;.] | . |
| Days of Treatment (1st Treatment) | 125 [68.0;223] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Days of Treatment (2nd Treatment) | 119 [57.0;204] | 137 [76.0;239] | 139 [83.0;245] | <0.001 |
| Days of Treatment (3rd Treatment) | 127 [71.0;220] | 127 [70.0;229] | 148 [84.8;246] | 0.020 |
| Days of Treatment (4th Treatment) | 143 [57.2;248] | 125 [70.0;215] | 131 [71.0;231] | 0.997 |
| Days of Treatment (5th Treatment) | 199 [139;335] | 138 [67.8;254] | 145 [59.2;260] | 0.567 |
| Days of Treatment (6th Treatment) | 28.0 [17.5;38.5] | 149 [121;195] | 116 [68.5;205] | 0.121 |
| Days of Treatment (7th Treatment) | 16.5 [13.2;19.8] | 169 [58.5;244] | 53.0 [52.0;120] | 0.075 |
| Days of Treatment (8th Treatment) | . [.;.] | 84.5 [62.2;107] | 19.0 [19.0;19.0] | 0.221 |
| Days of Treatment (9th Treatment) | . [.;.] | 211 [211;211] | . [.;.] | . |
| Days of Treatment (10th Treatment) | . [.;.] | 219 [219;219] | . [.;.] | . |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.812 |
| Cum. Diff Between Treatments (2nd Treatment) | 715 [380;1315] | 779 [396;1355] | 872 [441;1492] | 0.075 |
| Cum. Diff Between Treatments (3rd Treatment) | 920 [405;1812] | 1128 [674;1648] | 1235 [770;1697] | 0.436 |
| Cum. Diff Between Treatments (4th Treatment) | 1290 [1044;2121] | 1196 [843;1722] | 1448 [1149;2190] | 0.034 |
| Cum. Diff Between Treatments (5th Treatment) | 2020 [1858;2182] | 1470 [1091;1768] | 1418 [944;2384] | 0.513 |
| Cum. Diff Between Treatments (6th Treatment) | 2536 [2415;2657] | 1576 [1244;2085] | 1221 [1015;1287] | 0.174 |
| Cum. Diff Between Treatments (7th Treatment) | . [.;.] | 1418 [1300;1537] | 1188 [1188;1188] | 1.000 |
| Cum. Diff Between Treatments (8th Treatment) | . [.;.] | 1706 [1706;1706] | . [.;.] | . |
| Cum. Diff Between Treatments (9th Treatment) | . [.;.] | 1944 [1944;1944] | . [.;.] | . |
| Cum. Diff Between Treatments (10th Treatment) | . | . | . | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | . | |||
| 1 | 1033 (77.4%) | 11903 (77.0%) | 5536 (77.0%) | |
| 2 | 207 (15.5%) | 2540 (16.4%) | 1161 (16.1%) | |
| 3 | 71 (5.32%) | 673 (4.35%) | 343 (4.77%) | |
| 4 | 15 (1.12%) | 233 (1.51%) | 99 (1.38%) | |
| 5 | 7 (0.52%) | 69 (0.45%) | 35 (0.49%) | |
| 6 | 0 (0.00%) | 27 (0.17%) | 10 (0.14%) | |
| 7 | 2 (0.15%) | 8 (0.05%) | 4 (0.06%) | |
| 8 | 0 (0.00%) | 1 (0.01%) | 1 (0.01%) | |
| 10 | 0 (0.00%) | 1 (0.01%) | 0 (0.00%) | |
| Max. Cumulative Days of Treatment | 154 [80.0;304] | 183 [98.0;332] | 186 [100;338] | <0.001 |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.883 |
| Total count of Psychiatric & Drug dependence Diagnostics: | . | |||
| 0 | 224 (16.8%) | 2493 (16.1%) | 1547 (21.5%) | |
| 1 | 695 (52.1%) | 8175 (52.9%) | 3827 (53.2%) | |
| 2 | 399 (29.9%) | 4536 (29.3%) | 1742 (24.2%) | |
| 3 | 16 (1.20%) | 226 (1.46%) | 67 (0.93%) | |
| 4 | 1 (0.07%) | 25 (0.16%) | 6 (0.08%) | |
| Total count of Psychiatric Diagnostics: | . | |||
| 0 | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| 1 | 860 (64.4%) | 9441 (61.1%) | 4013 (55.8%) | |
| 2 | 19 (1.42%) | 264 (1.71%) | 82 (1.14%) | |
| 3 | 3 (0.22%) | 32 (0.21%) | 6 (0.08%) | |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | . | |||
| 0 | 799 (59.9%) | 9040 (58.5%) | 4618 (64.2%) | |
| 1 | 514 (38.5%) | 6119 (39.6%) | 2483 (34.5%) | |
| 2 | 19 (1.42%) | 264 (1.71%) | 82 (1.14%) | |
| 3 | 3 (0.22%) | 32 (0.21%) | 6 (0.08%) | |
| Days of Treatment (Fourth or those that follow) | 149 [51.8;234] | 138 [77.8;225] | 137 [85.4;236] | 0.966 |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | 464 [238;588] | 258 [112;416] | 288 [164;682] | 0.064 |
| Cumulative Days of Treatment (Fourth or those that follow) | 590 [364;936] | 683 [454;932] | 727 [518;999] | 0.083 |
| Cumulative Difference Between Treatments (Fifth or those that follow) | 1760 [1044;2121] | 1345 [943;1759] | 1518 [1174;2252] | 0.081 |
| Average Cumulative Days of Treatment (Fourth or those that follow) | 133 [91.0;209] | 162 [107;218] | 171 [116;229] | 0.080 |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | 348 [261;497] | 312 [222;424] | 373 [286;544] | 0.064 |
| Comorbidity ICD-10 (with amount of different diagnosis): | <0.001 | |||
| Without psychiatric comorbidity | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| Diagnosis unknown (under study) | 346 (25.9%) | 3322 (21.5%) | 1530 (21.3%) | |
| One | 514 (38.5%) | 6119 (39.6%) | 2483 (34.5%) | |
| Two or more | 22 (1.65%) | 296 (1.92%) | 88 (1.22%) | |
| No. of treatments with 18+ at admission between 2010 and 2019: | 0.426 | |||
| 01 | 1034 (77.5%) | 11905 (77.0%) | 5536 (77.0%) | |
| 02 | 206 (15.4%) | 2540 (16.4%) | 1161 (16.1%) | |
| 03 | 71 (5.32%) | 671 (4.34%) | 343 (4.77%) | |
| 04 or more | 24 (1.80%) | 339 (2.19%) | 149 (2.07%) | |
| Sexual abuse: | 0.034 | |||
| No sexual abuse | 1053 (78.9%) | 12207 (79.0%) | 5620 (78.2%) | |
| Sexual abuse | 27 (2.02%) | 175 (1.13%) | 87 (1.21%) | |
| ‘Missing’ | 255 (19.1%) | 3073 (19.9%) | 1482 (20.6%) | |
| Domestic violence: | <0.001 | |||
| No domestic violence | 745 (55.8%) | 9509 (61.5%) | 4050 (56.3%) | |
| Domestic violence | 335 (25.1%) | 2873 (18.6%) | 1657 (23.0%) | |
| ‘Missing’ | 255 (19.1%) | 3073 (19.9%) | 1482 (20.6%) | |
| Note. Variables of C1 dataset had to be standardized before comparison; | ||||
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | ||||
| Categorical variables are presented as number (%) |
Show code
#janitor::tabyl(prueba2_imp$numero_de_hijos_mod_joel)Outcomes
Univariate or Bivariate Analyses
Here, we compared the incidence rate of those who experienced 1, 2, 3, 1 & 2, 1 to 3, 4 or more, and dropouts in the first treatment, between different “living with” patterns.
Show code
invisible("https://cran.r-project.org/web/packages/riskCommunicator/vignettes/Vignette_newbieRusers.html#continuous-exposure-example")
prueba2_imp2<-
cbind.data.frame(prueba2_imp,
dplyr::select(prueba2,cum_dias_trat_sin_na_1, cum_dias_trat_sin_na_2, cum_dias_trat_sin_na_3, cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10, dias_treat_imp_sin_na_1, dias_treat_imp_sin_na_2, dias_treat_imp_sin_na_3, dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10, cum_diff_bet_treat_1, cum_diff_bet_treat_2, cum_diff_bet_treat_3, cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10),
dplyr::select(prueba2, fech_ing, fech_egres_imp, sus_principal_mod, cat_ocupacional_corr, cnt_diagnostico_trs_fisico, cnt_otros_probl_at_sm_or, dg_cie_10_rec, abandono_temprano, duplicates_filtered, max_cum_dias_trat_sin_na, max_cum_diff_bet_treat, cnt_mod_cie_10_dg_cons_sus_or, cnt_mod_cie_10_or, dg_total_cie_10, dias_treat_imp_sin_na_four, diff_bet_treat_four, cum_dias_trat_sin_na_four, cum_diff_bet_treat_four, mean_cum_dias_trat_sin_na_four, mean_cum_diff_bet_treat_four, n_treats, sex_abuse, dom_violence, motivodeegreso_mod_imp)) %>%
dplyr::mutate(person_years=(as.numeric(as.Date("2019-11-13"))- as.numeric(fech_ing))/365.25,
#2023-02-02
event= dplyr::case_when(grepl("Drop-out|Administrative", motivodeegreso_mod_imp)~1, T~0),
yrs_to_tr_dropout= dplyr::case_when(event==1~ dias_treat_imp_sin_na_1/365.25, T~ (as.numeric(as.Date("2019-11-13"))- as.numeric(fech_ing))/365.25),
#2023-02-06
event_cmprsk= dplyr::case_when(grepl("Drop-out|Administrative", motivodeegreso_mod_imp)~1, grepl("Therapeutic", motivodeegreso_mod_imp)~2, T~0),
yrs_to_tr_dropout_cmprsk= dplyr::case_when(event%in% c(1,2)~ dias_treat_imp_sin_na_1/365.25, T~ (as.numeric(as.Date("2019-11-13"))- as.numeric(fech_ing))/365.25),
max_cum_dias_trat_sin_na_adj= max_cum_dias_trat_sin_na/person_years,
duplicates_filtered_adj= duplicates_filtered/person_years)#
#n_treats= readmissiones
tablas_inc<-
rbind(data.frame(biostat3::survRate(Surv(person_years, n_treats=="01") ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, n_treats=="02") ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, n_treats=="03") ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, grepl("01|02",n_treats)) ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, grepl("01|02|03",n_treats)) ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, n_treats=="04 or more") ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, grepl("Drop", motivodeegreso_mod_imp)) ~ con_quien_vive_joel, data=prueba2_imp2)))
data.table::data.table(tablas_inc, keep.rownames = F)%>%
dplyr::mutate(rate=round(rate*1000,0),lower=round(lower*1000,0), upper=round(upper*1000,0))%>%
knitr::kable(format="html",caption= "Summary of incidence rates of events depending on whom patients live with")%>%
kableExtra::add_footnote(c("Note. Events per 1,000 person-years"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::group_rows("Only one treatment",1,3)%>%
kableExtra::group_rows("Two treatments",4,6)%>%
kableExtra::group_rows("Three treatment",7,9)%>%
kableExtra::group_rows("One & two treatments",10,12)%>%
kableExtra::group_rows("One, two & three treatments",13,15)%>%
kableExtra::group_rows("Four or more treatments",16,18)%>%
kableExtra::group_rows("Drop-out from the first treatment",19,21)%>%
kableExtra::scroll_box(width = "100%", height = "600px")| con_quien_vive_joel | tstop | event | rate | lower | upper |
|---|---|---|---|---|---|
| Only one treatment | |||||
| Alone | 6878.207 | 1034 | 150 | 141 | 160 |
| Family of origin | 78666.565 | 11905 | 151 | 149 | 154 |
| With couple/children | 37054.174 | 5536 | 149 | 145 | 153 |
| Two treatments | |||||
| Alone | 6878.207 | 206 | 30 | 26 | 34 |
| Family of origin | 78666.565 | 2540 | 32 | 31 | 34 |
| With couple/children | 37054.174 | 1161 | 31 | 30 | 33 |
| Three treatment | |||||
| Alone | 6878.207 | 71 | 10 | 8 | 13 |
| Family of origin | 78666.565 | 671 | 9 | 8 | 9 |
| With couple/children | 37054.174 | 343 | 9 | 8 | 10 |
| One & two treatments | |||||
| Alone | 6878.207 | 1240 | 180 | 170 | 191 |
| Family of origin | 78666.565 | 14445 | 184 | 181 | 187 |
| With couple/children | 37054.174 | 6697 | 181 | 176 | 185 |
| One, two & three treatments | |||||
| Alone | 6878.207 | 1311 | 191 | 180 | 201 |
| Family of origin | 78666.565 | 15116 | 192 | 189 | 195 |
| With couple/children | 37054.174 | 7040 | 190 | 186 | 194 |
| Four or more treatments | |||||
| Alone | 6878.207 | 24 | 3 | 2 | 5 |
| Family of origin | 78666.565 | 339 | 4 | 4 | 5 |
| With couple/children | 37054.174 | 149 | 4 | 3 | 5 |
| Drop-out from the first treatment | |||||
| Alone | 6878.207 | 766 | 111 | 104 | 120 |
| Family of origin | 78666.565 | 8691 | 110 | 108 | 113 |
| With couple/children | 37054.174 | 4417 | 119 | 116 | 123 |
| Note. Events per 1,000 person-years | |||||
The amount of treatments (duplicates_filtered) and the maximum cumulative days spent in treatment (max_cum_dias_trat_sin_na) by each user were adjusted by dividing them by the years they were available in the study (we subtracted the date of the first admission to the date of the end of the follow-up).
Show code
rbind(
data.table::data.table(finalfit(prueba2_imp2,'max_cum_dias_trat_sin_na_adj', "con_quien_vive_joel"))[,2:5],
data.table::data.table(finalfit(prueba2_imp2,'duplicates_filtered_adj', "con_quien_vive_joel"))[,2:5], fill= T
) %>%
knitr::kable(format="html",caption= "Summary of adjusted measures, depending on whom patients live with") %>%
kableExtra::kable_classic() %>%
kableExtra::group_rows("Max. Cumulative Days of Treatment",1,3)%>%
kableExtra::group_rows("Number of Treatments by User (Total)",4,6)%>%
kableExtra::add_footnote(c("Note. Outcomes were adjusted for person-years", paste0("First regression= ", ff_metrics(lm(max_cum_dias_trat_sin_na_adj~ con_quien_vive_joel, prueba2_imp2))),paste0("Second regression= ",ff_metrics(lm(duplicates_filtered_adj~ con_quien_vive_joel, prueba2_imp2))
)), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "350px")| unit | value | OR (univariable) | |
|---|---|---|---|
| Max. Cumulative Days of Treatment | |||
| Alone | Mean (sd) | 70.6 (93.5) |
|
| Family of origin | Mean (sd) | 80.1 (91.9) | 9.49 (4.44 to 14.55, p<0.001) |
| With couple/children | Mean (sd) | 75.9 (86.6) | 5.26 (-0.02 to 10.55, p=0.051) |
| Number of Treatments by User (Total) | |||
| Alone | Mean (sd) | 0.5 (1.1) |
|
| Family of origin | Mean (sd) | 0.6 (1.1) | 0.01 (-0.05 to 0.08, p=0.636) |
| With couple/children | Mean (sd) | 0.5 (1.0) | -0.03 (-0.10 to 0.03, p=0.323) |
| Note. Outcomes were adjusted for person-years | |||
| First regression= Number in dataframe = 23979, Number in model = 23979, Missing = 0, Log-likelihood = -142049.04, AIC = 284106.1, R-squared = 0.00087, Adjusted R-squared = 0.00079 | |||
| Second regression= Number in dataframe = 23979, Number in model = 23979, Missing = 0, Log-likelihood = -36286.27, AIC = 72580.5, R-squared = 0.00038, Adjusted R-squared = 0.00029 | |||
We checked the linear model with adjusted cumulative days of treatment
Show code
cat("Autocorrelation?") Autocorrelation?Show code
lmtest::dwtest(prueba2_imp2$max_cum_dias_trat_sin_na_adj~ prueba2_imp2$con_quien_vive_joel)
Durbin-Watson test
data: prueba2_imp2$max_cum_dias_trat_sin_na_adj ~ prueba2_imp2$con_quien_vive_joel
DW = 1.9797, p-value = 0.0583
alternative hypothesis: true autocorrelation is greater than 0Show code
cat("Pattern in residuals?") Pattern in residuals?Show code
Show code
cat("Are variances equal?") Are variances equal?Show code
car::leveneTest(prueba2_imp2$max_cum_dias_trat_sin_na_adj,as.factor(prueba2_imp2$con_quien_vive_joel), center = mean)Levene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 2 16.816 5.037e-08 ***
23976
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Show code
#Como el estadístico de Levene arroja un p-valor inferior a 0,05
#(Pr>F = 0,023) se rechaza la hipótesis nula de igualdad de varianzas.
mod<-aov(prueba2_imp2$max_cum_dias_trat_sin_na_adj~as.factor(prueba2_imp2$con_quien_vive_joel))
contraste <- rbind(c(1,1,-2),c(1,-1,0)) #
#Se entrega al programa los contrastes a utilizar:
filas<-c("Alone & Family of origin vs With couple/children","Alone vs Family of origin")
columnas<-c("Alone","Family of origin","With couple/children")
# contraste se definen un vector de números de acuerdo a las hipótesis formuladas
# función glht que tiene como argumento el modelo ANOVA definido como mod,
# y definir la variable grupal estado_nutricional= contraste. Por último, realizar un summary.
dimnames(contraste)<-list(filas,columnas)
cat("GLHT: User-defined General linear hypothesis") GLHT: User-defined General linear hypothesisShow code
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: User-defined Contrasts
Fit: aov(formula = prueba2_imp2$max_cum_dias_trat_sin_na_adj ~ as.factor(prueba2_imp2$con_quien_vive_joel))
Linear Hypotheses:
Estimate
Alone & Family of origin vs With couple/children == 0 -1.033
Alone vs Family of origin == 0 -9.494
Std. Error
Alone & Family of origin vs With couple/children == 0 3.349
Alone vs Family of origin == 0 2.581
t value
Alone & Family of origin vs With couple/children == 0 -0.308
Alone vs Family of origin == 0 -3.679
Pr(>|t|)
Alone & Family of origin vs With couple/children == 0 0.924666
Alone vs Family of origin == 0 0.000453 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)Show code
cat("Not assuming equal variances") Not assuming equal variancesShow code
oneway.test(max_cum_dias_trat_sin_na_adj~as.factor(con_quien_vive_joel),
data = prueba2_imp2,
var.equal=FALSE)
One-way analysis of means (not assuming equal variances)
data: max_cum_dias_trat_sin_na_adj and as.factor(con_quien_vive_joel)
F = 10.3, num df = 2.0, denom df = 3573.5, p-value = 3.463e-05Show code
cat("Tukey HSD")Tukey HSDShow code
TukeyHSD(mod) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = prueba2_imp2$max_cum_dias_trat_sin_na_adj ~ as.factor(prueba2_imp2$con_quien_vive_joel))
$`as.factor(prueba2_imp2$con_quien_vive_joel)`
diff lwr upr
Family of origin-Alone 9.494373 3.445382 15.543364
With couple/children-Alone 5.263452 -1.056002 11.582906
With couple/children-Family of origin -4.230921 -7.258117 -1.203725
p adj
Family of origin-Alone 0.0006866
With couple/children-Alone 0.1243273
With couple/children-Family of origin 0.0030289Show code
cat("Games-Howell") Games-HowellShow code
posthocTGH(prueba2_imp2$con_quien_vive_joel,
y = prueba2_imp2$max_cum_dias_trat_sin_na_adj, method= "games-howell", digits=4) n means variances
Alone 1335 70.63 8749
Family of origin 15455 80.12 8454
With couple/children 7189 75.89 7501
diff ci.lo ci.hi t
Family of origin-Alone 9.494 3.243 15.746 3.563
With couple/children-Alone 5.263 -1.202 11.729 1.910
With couple/children-Family of origin -4.231 -7.187 -1.275 3.355
df p
Family of origin-Alone 1565 .0011
With couple/children-Alone 1784 .1362
With couple/children-Family of origin 14807 .0023Show code
#https://www.usabart.nl/eval/5-2%20ANOVA.pdfSurvival Analyses (Feb 2023)
Explore
Show code
#make dummy for each treatment arm
prueba2_imp2$con_quien_vive_joel_fam_or<-as.numeric(prueba2_imp2$con_quien_vive_joel=="Family of origin")
prueba2_imp2$con_quien_vive_joel_alone<-as.numeric(prueba2_imp2$con_quien_vive_joel=="Alone")
prueba2_imp2$con_quien_vive_joel_coup_chil<-as.numeric(prueba2_imp2$con_quien_vive_joel=="With couple/children")
biostat3::survRate(Surv(yrs_to_tr_dropout, event) ~ con_quien_vive_joel, data= prueba2_imp2) %>%
#dplyr::mutate(rate=round(rate*1000,0),lower=round(lower*1000,0), upper=round(upper*1000,0))%>%
knitr::kable("markdown", caption= "Glimpse of the survival analysis, Years to drop-out in the First Treatment by Living Conditions")| con_quien_vive_joel | tstop | event | rate | lower | upper | |
|---|---|---|---|---|---|---|
| con_quien_vive_joel=Alone | Alone | 2182.976 | 926 | 0.4241916 | 0.3973065 | 0.4524172 |
| con_quien_vive_joel=Family of origin | Family of origin | 27734.735 | 10209 | 0.3680944 | 0.3609883 | 0.3753052 |
| con_quien_vive_joel=With couple/children | With couple/children | 11721.960 | 5076 | 0.4330334 | 0.4212017 | 0.4451131 |
Show code
dropout_fit<- survfit(Surv(yrs_to_tr_dropout, event) ~ con_quien_vive_joel, data= prueba2_imp2,
type = "kaplan-meier",
error = "greenwood",
conf.type = "log-log")
survminer::ggsurvplot(dropout_fit,
#fun = "cumhaz",
conf.int = TRUE,
legend.labs = c("Alone", "Family of origin", "With couple/children"),
risk.table = "abs_pct",
#ncensor.plot = TRUE,
ggtheme = theme_classic(base_size=15),
risk.table.y.text.col = F,
risk.table.col="black",
font.tickslab = c(10),
risk.table.height = .2,
risk.table.fontsize = 2.5,
#break.time.by = 365.25,
pval = T,
#ylim=c(0,10),
legend = c(0.75, 0.8),
legend.title="Living conditions",
xlab= "Time (in years)",
#cumevents=T,
surv.connect = T,
censor= F
)
Figure 5: Kaplan-Meier curves by Living conditions
We calculated the restricted mean survival time and lost, considering that proportionality of trends could be violated.
Show code
cat("Living with Family of origin vs. Alone and With couple/children at 6 months")Living with Family of origin vs. Alone and With couple/children at 6 monthsShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, as.numeric(prueba2_imp2$con_quien_vive_joel=="Family of origin"), tau=.5)
The truncation time: tau = 0.5 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 0.387 0.001 0.384 0.389
RMST (arm=0) 0.381 0.002 0.378 0.384
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 0.113 0.001 0.111 0.116
RMTL (arm=0) 0.119 0.002 0.116 0.122
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) 0.005 0.002 0.009 0.005
RMST (arm=1)/(arm=0) 1.014 1.004 1.025 0.006
RMTL (arm=1)/(arm=0) 0.954 0.922 0.986 0.005Show code
cat("Living with Family of origin vs. Alone and With couple/children at 1 year")Living with Family of origin vs. Alone and With couple/children at 1 yearShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, as.numeric(prueba2_imp2$con_quien_vive_joel=="Family of origin"), tau=1)
The truncation time: tau = 1 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 0.599 0.003 0.593 0.604
RMST (arm=0) 0.575 0.004 0.568 0.583
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 0.401 0.003 0.396 0.407
RMTL (arm=0) 0.425 0.004 0.417 0.432
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) 0.024 0.014 0.033 0
RMST (arm=1)/(arm=0) 1.041 1.024 1.058 0
RMTL (arm=1)/(arm=0) 0.945 0.924 0.966 0Show code
cat("Living with Family of origin vs. Alone and With couple/children at 3 years")Living with Family of origin vs. Alone and With couple/children at 3 yearsShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, as.numeric(prueba2_imp2$con_quien_vive_joel=="Family of origin"), tau=3)
The truncation time: tau = 3 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 1.244 0.010 1.224 1.264
RMST (arm=0) 1.134 0.013 1.108 1.159
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 1.756 0.010 1.736 1.776
RMTL (arm=0) 1.866 0.013 1.841 1.892
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) 0.110 0.078 0.143 0
RMST (arm=1)/(arm=0) 1.097 1.068 1.128 0
RMTL (arm=1)/(arm=0) 0.941 0.924 0.958 0Show code
cat("===============================================================================")===============================================================================Show code
cat("Living Alone vs. Family of origin and With couple/children at 6 months")Living Alone vs. Family of origin and With couple/children at 6 monthsShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, prueba2_imp2$con_quien_vive_joel_alone, tau=.5)
The truncation time: tau = 0.5 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 0.358 0.004 0.350 0.367
RMST (arm=0) 0.386 0.001 0.384 0.388
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 0.142 0.004 0.133 0.150
RMTL (arm=0) 0.114 0.001 0.112 0.116
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) -0.028 -0.037 -0.019 0
RMST (arm=1)/(arm=0) 0.927 0.905 0.950 0
RMTL (arm=1)/(arm=0) 1.247 1.170 1.328 0Show code
cat("Living Alone vs. Family of origin and With couple/children at 1 year")Living Alone vs. Family of origin and With couple/children at 1 yearShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, prueba2_imp2$con_quien_vive_joel_alone, tau=1)
The truncation time: tau = 1 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 0.544 0.010 0.524 0.563
RMST (arm=0) 0.593 0.002 0.589 0.598
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 0.456 0.010 0.437 0.476
RMTL (arm=0) 0.407 0.002 0.402 0.411
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) -0.050 -0.070 -0.029 0
RMST (arm=1)/(arm=0) 0.916 0.883 0.951 0
RMTL (arm=1)/(arm=0) 1.122 1.073 1.173 0Show code
cat("Living Alone vs. Family of origin and With couple/children at 3 years")Living Alone vs. Family of origin and With couple/children at 3 yearsShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, prueba2_imp2$con_quien_vive_joel_alone, tau=3)
The truncation time: tau = 3 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 1.116 0.034 1.050 1.182
RMST (arm=0) 1.210 0.008 1.194 1.226
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 1.884 0.034 1.818 1.950
RMTL (arm=0) 1.790 0.008 1.774 1.806
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) -0.094 -0.162 -0.025 0.007
RMST (arm=1)/(arm=0) 0.923 0.868 0.981 0.009
RMTL (arm=1)/(arm=0) 1.052 1.015 1.091 0.006Show code
cat("===============================================================================")===============================================================================Show code
cat("Living with With couple/children(1) vs. Alone and Family of origin at 6 months")Living with With couple/children(1) vs. Alone and Family of origin at 6 monthsShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, prueba2_imp2$con_quien_vive_joel_coup_chil, tau=.5)
The truncation time: tau = 0.5 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 0.385 0.002 0.382 0.389
RMST (arm=0) 0.384 0.001 0.382 0.387
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 0.115 0.002 0.111 0.118
RMTL (arm=0) 0.116 0.001 0.113 0.118
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) 0.001 -0.003 0.005 0.621
RMST (arm=1)/(arm=0) 1.003 0.992 1.013 0.621
RMTL (arm=1)/(arm=0) 0.991 0.957 1.026 0.622Show code
cat("Living with With couple/children(1) vs. Alone and Family of origin at 1 year")Living with With couple/children(1) vs. Alone and Family of origin at 1 yearShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, prueba2_imp2$con_quien_vive_joel_coup_chil, tau=1)
The truncation time: tau = 1 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 0.581 0.004 0.573 0.589
RMST (arm=0) 0.594 0.003 0.589 0.600
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 0.419 0.004 0.411 0.427
RMTL (arm=0) 0.406 0.003 0.400 0.411
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) -0.013 -0.023 -0.004 0.007
RMST (arm=1)/(arm=0) 0.978 0.962 0.994 0.007
RMTL (arm=1)/(arm=0) 1.033 1.009 1.057 0.007Show code
cat("Living with With couple/children(1) vs. Alone and Family of origin at 3 years")Living with With couple/children(1) vs. Alone and Family of origin at 3 yearsShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, prueba2_imp2$con_quien_vive_joel_coup_chil, tau=3)
The truncation time: tau = 3 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 1.137 0.014 1.109 1.164
RMST (arm=0) 1.234 0.010 1.215 1.253
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 1.863 0.014 1.836 1.891
RMTL (arm=0) 1.766 0.010 1.747 1.785
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) -0.097 -0.131 -0.064 0
RMST (arm=1)/(arm=0) 0.921 0.895 0.948 0
RMTL (arm=1)/(arm=0) 1.055 1.036 1.074 0Show code
#https://rpubs.com/linpearl89/TTE-RCT
#https://search.r-project.org/CRAN/refmans/adjustedCurves/html/surv_aiptw_pseudo.html
#https://search.r-project.org/CRAN/refmans/adjustedCurves/html/surv_iptw_cox.html
#https://cran.r-project.org/web/packages/RISCA/RISCA.pdf
prueba2_imp2<-
dplyr::mutate(prueba2_imp2,numero_de_hijos_mod_joel= as.ordered(numero_de_hijos_mod_joel)) %>% dplyr::mutate(freq_cons_sus_prin=ordered(freq_cons_sus_prin,levels=c("Did not use", "Less than 1 day a week", "1 day a week or more", "2 to 3 days a week","4 to 6 days a week", "Daily")))
coxfit <- coxph(Surv(yrs_to_tr_dropout, event) ~ con_quien_vive_joel + edad_al_ing+ sexo_2 + escolaridad_rec + sus_ini_mod_mvv + freq_cons_sus_prin + via_adm_sus_prin_act + condicion_ocupacional_corr+ compromiso_biopsicosocial + numero_de_hijos_mod_joel + tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod + tipo_centro+ cnt_mod_cie_10_dg_cons_sus_or + sus_principal_mod+ macrozona + cnt_mod_cie_10_or, ties="efron", data= prueba2_imp2)
survminer::ggcoxdiagnostics(coxfit, type = "schoenfeld")
Figure 6: Schoefeld residuals
Show code
ggsave("prueba_scho_plot.png", dpi=640, height=15, width= 15)Show code
data.frame(cox.zph(coxfit)$table) %>%
knitr::kable("markdown", caption= "Schoefeld residuals")| chisq | df | p | |
|---|---|---|---|
| con_quien_vive_joel | 64.692554 | 2 | 0.0000000 |
| edad_al_ing | 39.352315 | 1 | 0.0000000 |
| sexo_2 | 61.242208 | 1 | 0.0000000 |
| escolaridad_rec | 4.948839 | 2 | 0.0842119 |
| sus_ini_mod_mvv | 33.322975 | 4 | 0.0000010 |
| freq_cons_sus_prin | 190.059855 | 4 | 0.0000000 |
| via_adm_sus_prin_act | 45.900752 | 4 | 0.0000000 |
| condicion_ocupacional_corr | 225.182444 | 5 | 0.0000000 |
| compromiso_biopsicosocial | 137.029722 | 2 | 0.0000000 |
| numero_de_hijos_mod_joel | 27.132276 | 4 | 0.0000187 |
| tipo_de_plan_2_mod | 705.936689 | 2 | 0.0000000 |
| tenencia_de_la_vivienda_mod | 44.136105 | 4 | 0.0000000 |
| tipo_centro | 574.013144 | 1 | 0.0000000 |
| cnt_mod_cie_10_dg_cons_sus_or | 105.519418 | 1 | 0.0000000 |
| sus_principal_mod | 62.813227 | 4 | 0.0000000 |
| macrozona | 132.264481 | 2 | 0.0000000 |
| cnt_mod_cie_10_or | 690.849027 | 3 | 0.0000000 |
| GLOBAL | 2822.282319 | 46 | 0.0000000 |
Show code
#If the value of VIF is less than 1: no correlation - If the value of VIF is between 1-5, there is moderate correlation - If the value of VIF is above 5: severe correlation
#_#_#_#_#_#_#_#_#_
invisible("3.Cox, collinearity")
#global covs_3 "i.caus_disch_mod_imp_rec edad_al_ing_1 edad_ini_cons i.sex_enc i.esc_rec i.sus_prin_mod i.fr_sus_prin i.comp_biosoc i.ten_viv i.dg_cie_10_rec i.sud_severity_icd10 i.macrozone i.policonsumo i.n_off_vio i.n_off_acq i.n_off_sud "
#Error in Design(data, formula, specials = c("strat", "strata")) :
# Variable escolaridad_rec is an ordered factor with non-numeric levels.
# You should set options(contrasts=c("contr.treatment", "contr.treatment"))
#or rms will not work properly.
options(contrasts=c("contr.treatment", "contr.treatment"))
#https://randomeffect.net/post/2021/05/02/the-rms-validate-function/
f1 <- cph(Surv(yrs_to_tr_dropout, event) ~ con_quien_vive_joel + edad_al_ing+ sexo_2 + escolaridad_rec + sus_ini_mod_mvv + via_adm_sus_prin_act + condicion_ocupacional_corr+ compromiso_biopsicosocial + numero_de_hijos_mod_joel + tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod + tipo_centro+ cnt_mod_cie_10_dg_cons_sus_or + sus_principal_mod+ macrozona + cnt_mod_cie_10_or, data= prueba2_imp2, x=T, y=T)
warning("X matrix deemed to be singular; variable freq_cons_sus_prin=Daily --> I deleted it")
cvif <- rms::vif(f1)
#https://www.uvm.edu/~statdhtx/methods8/Supplements/R-Programs/AnovaExample.R
#http://faculty.nps.edu/sebuttre/home/r/contrasts.html
options(contrasts = c("contr.treatment","contr.poly")) # The default
cvif%>%
knitr::kable("markdown", caption= "Variance Inflation Factors in Cox Regressions")| x | |
|---|---|
| con_quien_vive_joel=Family of origin | 5.053409 |
| con_quien_vive_joel=With couple/children | 4.800690 |
| edad_al_ing | 1.186039 |
| sexo_2=Women | 1.278849 |
| escolaridad_rec=2-Completed high school or less | 1.388330 |
| escolaridad_rec=1-More than high school | 1.444238 |
| sus_ini_mod_mvv=Cocaine hydrochloride | 1.114386 |
| sus_ini_mod_mvv=Marijuana | 1.300702 |
| sus_ini_mod_mvv=Other | 1.063268 |
| sus_ini_mod_mvv=Cocaine paste | 1.146840 |
| via_adm_sus_prin_act=Intranasal (powder aspiration) | 23.952115 |
| via_adm_sus_prin_act=Injected Intravenously or Intramuscularly | 1.093075 |
| via_adm_sus_prin_act=Oral (drunk or eaten) | 22.671543 |
| via_adm_sus_prin_act=Other | 1.010756 |
| condicion_ocupacional_corr=Inactive | 1.254439 |
| condicion_ocupacional_corr=Looking for a job for the first time | 1.011435 |
| condicion_ocupacional_corr=No activity | 1.150679 |
| condicion_ocupacional_corr=Not seeking for work | 1.028571 |
| condicion_ocupacional_corr=Unemployed | 1.385616 |
| compromiso_biopsicosocial=2-Moderate | 3.288632 |
| compromiso_biopsicosocial=3-Severe | 3.942218 |
| numero_de_hijos_mod_joel=1 | 1.435590 |
| numero_de_hijos_mod_joel=2 | 1.497413 |
| numero_de_hijos_mod_joel=3 | 1.241735 |
| numero_de_hijos_mod_joel=4 or more | 1.137840 |
| tipo_de_plan_2_mod=PAI | 1.479464 |
| tipo_de_plan_2_mod=PR | 1.909074 |
| tenencia_de_la_vivienda_mod=Others | 3.349580 |
| tenencia_de_la_vivienda_mod=Owner/Transferred dwellings/Pays Dividends | 22.343064 |
| tenencia_de_la_vivienda_mod=Renting | 14.812411 |
| tenencia_de_la_vivienda_mod=Stays temporarily with a relative | 25.753407 |
| tipo_centro=Public | 1.377686 |
| cnt_mod_cie_10_dg_cons_sus_or | 1.778584 |
| sus_principal_mod=Cocaine hydrochloride | 50.201659 |
| sus_principal_mod=Marijuana | 17.129906 |
| sus_principal_mod=Other | 1.206447 |
| sus_principal_mod=Cocaine paste | 41.551780 |
| macrozona=North | 1.181710 |
| macrozona=South | 1.106510 |
| cnt_mod_cie_10_or | 1.639050 |
| cnt_mod_cie_10_or=2 | 1.124115 |
| cnt_mod_cie_10_or=3 | 1.036816 |
Show code
# dplyr::mutate_if(is.numeric,~round(.,2)) %>%
# DT::datatable()Adjusted
We calculated the restricted mean survival time and lost, considering that proportionality of trends could be violated.
Show code
vars<-c("edad_al_ing", "sexo_2", "escolaridad_rec", "sus_ini_mod_mvv", "freq_cons_sus_prin", "via_adm_sus_prin_act", "condicion_ocupacional_corr", "compromiso_biopsicosocial", "numero_de_hijos_mod_joel", "tipo_de_plan_2_mod", "tenencia_de_la_vivienda_mod", "tipo_centro", "cnt_mod_cie_10_dg_cons_sus_or", "sus_principal_mod", "macrozona", "cnt_mod_cie_10_or")
logit <- nnet::multinom(formula= as.formula(paste("con_quien_vive_joel~", paste(vars, collapse = "+"))),
data=prueba2_imp2)# weights: 141 (92 variable)
initial value 26343.624070
iter 10 value 17485.701165
iter 20 value 16827.142379
iter 30 value 16243.443454
iter 40 value 15466.183822
iter 50 value 15134.552925
iter 60 value 14981.048768
iter 70 value 14904.637381
iter 80 value 14862.046859
iter 90 value 14858.513163
final value 14858.477952
convergedShow code
pred <- predict(logit, type="probs")
prueba2_imp2$weight_mult_a00 <-WeightIt::get_w_from_ps(pred, prueba2_imp2$con_quien_vive_joel, estimand = "ATE", stabilize=T)
#truncate weights at 1%
prueba2_imp2$weight_tr <- ifelse(prueba2_imp2$weight_mult_a00 < quantile(prueba2_imp2$weight_mult_a00, probs=.01), quantile(prueba2_imp2$weight_mult_a00, probs=.01), prueba2_imp2$weight_mult_a00)
prueba2_imp2$weight_tr <- ifelse(prueba2_imp2$weight_mult_a00 > quantile(prueba2_imp2$weight_mult_a00, probs=.99), quantile(prueba2_imp2$weight_mult_a00, probs=.99), prueba2_imp2$weight_tr)
#truncate weights at 5%
prueba2_imp2$weight_tr2 <- ifelse(prueba2_imp2$weight_mult_a00 < quantile(prueba2_imp2$weight_mult_a00, probs=.05), quantile(prueba2_imp2$weight_mult_a00, probs=.05), prueba2_imp2$weight_mult_a00)
prueba2_imp2$weight_tr2 <- ifelse(prueba2_imp2$weight_mult_a00 > quantile(prueba2_imp2$weight_mult_a00, probs=.95), quantile(prueba2_imp2$weight_mult_a00, probs=.95), prueba2_imp2$weight_tr2)
#truncate weights at 10%
prueba2_imp2$weight_tr3 <- ifelse(prueba2_imp2$weight_mult_a00 < quantile(prueba2_imp2$weight_mult_a00, probs=.1), quantile(prueba2_imp2$weight_mult_a00, probs=.1), prueba2_imp2$weight_mult_a00)
prueba2_imp2$weight_tr3 <- ifelse(prueba2_imp2$weight_mult_a00 > quantile(prueba2_imp2$weight_mult_a00, probs=.9), quantile(prueba2_imp2$weight_mult_a00, probs=.9), prueba2_imp2$weight_tr3)
# AKM RMST adjusted for age
source("https://raw.githubusercontent.com/s-conner/akm-rmst/master/AKM_rmst.R")
cat("===============================================================================")===============================================================================Show code
cat("at 6 months (adjusted), not stabilized weights")at 6 months (adjusted), not stabilized weightsShow code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_mult_a00, tau=.5)
RMST calculated up to tau = 0.5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 0.381 0.008
Group Family of origin 0.385 0.001
Group With couple/children 0.378 0.003
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.004 0.008
Groups With couple/children vs. Alone -0.003 0.008
Groups With couple/children vs. Family of origin -0.007 0.003
CIL CIU
Groups Family of origin vs. Alone -0.012 0.020
Groups With couple/children vs. Alone -0.020 0.013
Groups With couple/children vs. Family of origin -0.013 -0.001
p
Groups Family of origin vs. Alone 0.627
Groups With couple/children vs. Alone 0.680
Groups With couple/children vs. Family of origin 0.017
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.010 0.021
Groups With couple/children vs. Alone -0.009 0.022
Groups With couple/children vs. Family of origin -0.019 0.008
Est. CIL CIU
Groups Family of origin vs. Alone 1.010 0.970 1.053
Groups With couple/children vs. Alone 0.991 0.949 1.034
Groups With couple/children vs. Family of origin 0.981 0.965 0.997
p
Groups Family of origin vs. Alone 0.629
Groups With couple/children vs. Alone 0.679
Groups With couple/children vs. Family of origin 0.018Show code
cat("At 1 year (adjusted), not stabilized weights")At 1 year (adjusted), not stabilized weightsShow code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_mult_a00, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 0.592 0.019
Group Family of origin 0.593 0.003
Group With couple/children 0.573 0.006
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.001 0.020
Groups With couple/children vs. Alone -0.020 0.020
Groups With couple/children vs. Family of origin -0.020 0.007
CIL CIU
Groups Family of origin vs. Alone -0.037 0.039
Groups With couple/children vs. Alone -0.059 0.020
Groups With couple/children vs. Family of origin -0.035 -0.006
p
Groups Family of origin vs. Alone 0.967
Groups With couple/children vs. Alone 0.334
Groups With couple/children vs. Family of origin 0.005
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.001 0.033
Groups With couple/children vs. Alone -0.034 0.034
Groups With couple/children vs. Family of origin -0.035 0.013
Est. CIL CIU
Groups Family of origin vs. Alone 1.001 0.939 1.068
Groups With couple/children vs. Alone 0.967 0.904 1.034
Groups With couple/children vs. Family of origin 0.966 0.942 0.990
p
Groups Family of origin vs. Alone 0.967
Groups With couple/children vs. Alone 0.327
Groups With couple/children vs. Family of origin 0.006Show code
cat("At 3 years (adjusted), not stabilized weights")At 3 years (adjusted), not stabilized weightsShow code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_mult_a00, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 1.258 0.070
Group Family of origin 1.219 0.012
Group With couple/children 1.143 0.022
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone -0.038 0.071
Groups With couple/children vs. Alone -0.114 0.074
Groups With couple/children vs. Family of origin -0.076 0.025
CIL CIU
Groups Family of origin vs. Alone -0.178 0.102
Groups With couple/children vs. Alone -0.259 0.031
Groups With couple/children vs. Family of origin -0.125 -0.026
p
Groups Family of origin vs. Alone 0.591
Groups With couple/children vs. Alone 0.123
Groups With couple/children vs. Family of origin 0.003
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone -0.031 0.057
Groups With couple/children vs. Alone -0.095 0.059
Groups With couple/children vs. Family of origin -0.064 0.022
Est. CIL CIU
Groups Family of origin vs. Alone 0.969 0.867 1.084
Groups With couple/children vs. Alone 0.909 0.809 1.021
Groups With couple/children vs. Family of origin 0.938 0.899 0.979
p
Groups Family of origin vs. Alone 0.585
Groups With couple/children vs. Alone 0.109
Groups With couple/children vs. Family of origin 0.003Show code
cat("===============================================================================")===============================================================================Show code
cat("at 6 months (adjusted), weights stabilized by 1%")at 6 months (adjusted), weights stabilized by 1%Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_tr, tau=.5)
RMST calculated up to tau = 0.5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 0.375 0.007
Group Family of origin 0.385 0.001
Group With couple/children 0.379 0.002
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.011 0.007
Groups With couple/children vs. Alone 0.004 0.007
Groups With couple/children vs. Family of origin -0.007 0.003
CIL CIU
Groups Family of origin vs. Alone -0.003 0.024
Groups With couple/children vs. Alone -0.010 0.018
Groups With couple/children vs. Family of origin -0.012 -0.001
p
Groups Family of origin vs. Alone 0.119
Groups With couple/children vs. Alone 0.567
Groups With couple/children vs. Family of origin 0.017
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.028 0.019
Groups With couple/children vs. Alone 0.011 0.019
Groups With couple/children vs. Family of origin -0.017 0.007
Est. CIL CIU
Groups Family of origin vs. Alone 1.029 0.992 1.067
Groups With couple/children vs. Alone 1.011 0.974 1.050
Groups With couple/children vs. Family of origin 0.983 0.969 0.997
p
Groups Family of origin vs. Alone 0.124
Groups With couple/children vs. Alone 0.568
Groups With couple/children vs. Family of origin 0.018Show code
cat("At 1 year (adjusted), weights stabilized by 1%")At 1 year (adjusted), weights stabilized by 1%Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_tr, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 0.575 0.016
Group Family of origin 0.594 0.003
Group With couple/children 0.574 0.006
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.019 0.016
Groups With couple/children vs. Alone -0.001 0.017
Groups With couple/children vs. Family of origin -0.020 0.007
CIL CIU
Groups Family of origin vs. Alone -0.013 0.052
Groups With couple/children vs. Alone -0.035 0.033
Groups With couple/children vs. Family of origin -0.033 -0.007
p
Groups Family of origin vs. Alone 0.242
Groups With couple/children vs. Alone 0.951
Groups With couple/children vs. Family of origin 0.002
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.033 0.029
Groups With couple/children vs. Alone -0.002 0.030
Groups With couple/children vs. Family of origin -0.035 0.011
Est. CIL CIU
Groups Family of origin vs. Alone 1.034 0.977 1.093
Groups With couple/children vs. Alone 0.998 0.941 1.058
Groups With couple/children vs. Family of origin 0.966 0.944 0.988
p
Groups Family of origin vs. Alone 0.249
Groups With couple/children vs. Alone 0.951
Groups With couple/children vs. Family of origin 0.002Show code
cat("At 3 years (adjusted), weights stabilized by 1%")At 3 years (adjusted), weights stabilized by 1%Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_tr, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 1.195 0.057
Group Family of origin 1.220 0.011
Group With couple/children 1.144 0.020
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.025 0.058
Groups With couple/children vs. Alone -0.051 0.060
Groups With couple/children vs. Family of origin -0.076 0.023
CIL CIU
Groups Family of origin vs. Alone -0.088 0.139
Groups With couple/children vs. Alone -0.169 0.067
Groups With couple/children vs. Family of origin -0.121 -0.031
p
Groups Family of origin vs. Alone 0.661
Groups With couple/children vs. Alone 0.400
Groups With couple/children vs. Family of origin 0.001
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.021 0.048
Groups With couple/children vs. Alone -0.043 0.051
Groups With couple/children vs. Family of origin -0.064 0.020
Est. CIL CIU
Groups Family of origin vs. Alone 1.021 0.929 1.123
Groups With couple/children vs. Alone 0.958 0.867 1.058
Groups With couple/children vs. Family of origin 0.938 0.902 0.975
p
Groups Family of origin vs. Alone 0.664
Groups With couple/children vs. Alone 0.392
Groups With couple/children vs. Family of origin 0.001Show code
cat("===============================================================================")===============================================================================Show code
cat("at 6 months (adjusted), weights stabilized by 5%")at 6 months (adjusted), weights stabilized by 5%Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_tr2, tau=.5)
RMST calculated up to tau = 0.5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 0.366 0.005
Group Family of origin 0.386 0.001
Group With couple/children 0.381 0.002
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.020 0.006
Groups With couple/children vs. Alone 0.015 0.006
Groups With couple/children vs. Family of origin -0.005 0.002
CIL CIU p
Groups Family of origin vs. Alone 0.009 0.031 0.000
Groups With couple/children vs. Alone 0.003 0.026 0.011
Groups With couple/children vs. Family of origin -0.010 0.000 0.031
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.053 0.015
Groups With couple/children vs. Alone 0.040 0.016
Groups With couple/children vs. Family of origin -0.014 0.006
Est. CIL CIU
Groups Family of origin vs. Alone 1.055 1.024 1.087
Groups With couple/children vs. Alone 1.041 1.009 1.074
Groups With couple/children vs. Family of origin 0.987 0.975 0.999
p
Groups Family of origin vs. Alone 0.000
Groups With couple/children vs. Alone 0.012
Groups With couple/children vs. Family of origin 0.032Show code
cat("At 1 year (adjusted), weights stabilized by 5%")At 1 year (adjusted), weights stabilized by 5%Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_tr2, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 0.553 0.013
Group Family of origin 0.595 0.003
Group With couple/children 0.576 0.005
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.042 0.013
Groups With couple/children vs. Alone 0.023 0.014
Groups With couple/children vs. Family of origin -0.019 0.006
CIL CIU
Groups Family of origin vs. Alone 0.016 0.067
Groups With couple/children vs. Alone -0.004 0.049
Groups With couple/children vs. Family of origin -0.030 -0.008
p
Groups Family of origin vs. Alone 0.001
Groups With couple/children vs. Alone 0.093
Groups With couple/children vs. Family of origin 0.001
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.072 0.023
Groups With couple/children vs. Alone 0.040 0.024
Groups With couple/children vs. Family of origin -0.032 0.010
Est. CIL CIU
Groups Family of origin vs. Alone 1.075 1.027 1.126
Groups With couple/children vs. Alone 1.041 0.993 1.092
Groups With couple/children vs. Family of origin 0.968 0.950 0.987
p
Groups Family of origin vs. Alone 0.002
Groups With couple/children vs. Alone 0.098
Groups With couple/children vs. Family of origin 0.001Show code
cat("At 3 years (adjusted), weights stabilized by 5%")At 3 years (adjusted), weights stabilized by 5%Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_tr2, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 1.126 0.043
Group Family of origin 1.222 0.011
Group With couple/children 1.143 0.017
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.096 0.044
Groups With couple/children vs. Alone 0.017 0.046
Groups With couple/children vs. Family of origin -0.079 0.020
CIL CIU
Groups Family of origin vs. Alone 0.009 0.183
Groups With couple/children vs. Alone -0.074 0.107
Groups With couple/children vs. Family of origin -0.118 -0.041
p
Groups Family of origin vs. Alone 0.030
Groups With couple/children vs. Alone 0.714
Groups With couple/children vs. Family of origin 0.000
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.082 0.039
Groups With couple/children vs. Alone 0.015 0.041
Groups With couple/children vs. Family of origin -0.067 0.017
Est. CIL CIU
Groups Family of origin vs. Alone 1.085 1.005 1.172
Groups With couple/children vs. Alone 1.015 0.937 1.100
Groups With couple/children vs. Family of origin 0.935 0.904 0.967
p
Groups Family of origin vs. Alone 0.036
Groups With couple/children vs. Alone 0.716
Groups With couple/children vs. Family of origin 0.000Show code
cat("===============================================================================")===============================================================================Show code
cat("at 6 months (adjusted), weights stabilized by 10%")at 6 months (adjusted), weights stabilized by 10%Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_tr3, tau=.5)
RMST calculated up to tau = 0.5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 0.363 0.005
Group Family of origin 0.386 0.001
Group With couple/children 0.382 0.002
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.023 0.005
Groups With couple/children vs. Alone 0.019 0.005
Groups With couple/children vs. Family of origin -0.004 0.002
CIL CIU p
Groups Family of origin vs. Alone 0.013 0.033 0.000
Groups With couple/children vs. Alone 0.008 0.029 0.000
Groups With couple/children vs. Family of origin -0.009 0.000 0.064
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.061 0.014
Groups With couple/children vs. Alone 0.050 0.015
Groups With couple/children vs. Family of origin -0.011 0.006
Est. CIL CIU
Groups Family of origin vs. Alone 1.063 1.034 1.093
Groups With couple/children vs. Alone 1.052 1.022 1.082
Groups With couple/children vs. Family of origin 0.989 0.978 1.001
p
Groups Family of origin vs. Alone 0.000
Groups With couple/children vs. Alone 0.001
Groups With couple/children vs. Family of origin 0.064Show code
cat("At 1 year (adjusted), weights stabilized by 10%")At 1 year (adjusted), weights stabilized by 10%Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_tr3, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 0.548 0.011
Group Family of origin 0.595 0.003
Group With couple/children 0.577 0.005
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.047 0.012
Groups With couple/children vs. Alone 0.029 0.012
Groups With couple/children vs. Family of origin -0.018 0.005
CIL CIU
Groups Family of origin vs. Alone 0.024 0.070
Groups With couple/children vs. Alone 0.005 0.053
Groups With couple/children vs. Family of origin -0.028 -0.007
p
Groups Family of origin vs. Alone 0.000
Groups With couple/children vs. Alone 0.018
Groups With couple/children vs. Family of origin 0.001
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.082 0.021
Groups With couple/children vs. Alone 0.052 0.022
Groups With couple/children vs. Family of origin -0.030 0.009
Est. CIL CIU
Groups Family of origin vs. Alone 1.085 1.040 1.132
Groups With couple/children vs. Alone 1.053 1.008 1.100
Groups With couple/children vs. Family of origin 0.970 0.953 0.988
p
Groups Family of origin vs. Alone 0.000
Groups With couple/children vs. Alone 0.021
Groups With couple/children vs. Family of origin 0.001Show code
cat("At 3 years (adjusted), weights stabilized by 10%")At 3 years (adjusted), weights stabilized by 10%Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group= factor(prueba2_imp2$con_quien_vive_joel), weight=prueba2_imp2$weight_tr3, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 1.113 0.039
Group Family of origin 1.223 0.010
Group With couple/children 1.141 0.016
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.110 0.040
Groups With couple/children vs. Alone 0.028 0.042
Groups With couple/children vs. Family of origin -0.082 0.019
CIL CIU
Groups Family of origin vs. Alone 0.031 0.188
Groups With couple/children vs. Alone -0.054 0.109
Groups With couple/children vs. Family of origin -0.119 -0.045
p
Groups Family of origin vs. Alone 0.006
Groups With couple/children vs. Alone 0.508
Groups With couple/children vs. Family of origin 0.000
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.094 0.036
Groups With couple/children vs. Alone 0.024 0.037
Groups With couple/children vs. Family of origin -0.069 0.016
Est. CIL CIU
Groups Family of origin vs. Alone 1.098 1.024 1.178
Groups With couple/children vs. Alone 1.025 0.953 1.102
Groups With couple/children vs. Family of origin 0.933 0.904 0.963
p
Groups Family of origin vs. Alone 0.009
Groups With couple/children vs. Alone 0.512
Groups With couple/children vs. Family of origin 0.000Show code
#(3) (PDF) Adjusted restricted mean survival times in observational studies. Available from: https://www.researchgate.net/publication/333326572_Adjusted_restricted_mean_survival_times_in_observational_studies [accessed Feb 02 2023].
logit <- glm(formula= as.formula(paste("con_quien_vive_joel_fam_or~", paste(vars, collapse = "+"))),
data=prueba2_imp2, family=binomial(link= "logit"))
pred <- predict(logit, type="response")
prueba2_imp2$weight <- prueba2_imp2$con_quien_vive_joel_fam_or/pred + (1 - prueba2_imp2$con_quien_vive_joel_fam_or)/(1-pred)
#https://anthonyongphd.files.wordpress.com/2016/02/emerging-adulthood-2016-thoemmes-40-59.pdf
#A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models
#truncate weights at 1%
cat("Stabilized weights")
prueba2_imp2$weight_tr <- ifelse(prueba2_imp2$weight < quantile(prueba2_imp2$weight, probs=.01), quantile(prueba2_imp2$weight, probs=.01), prueba2_imp2$weight)
prueba2_imp2$weight_tr <- ifelse(prueba2_imp2$weight > quantile(prueba2_imp2$weight, probs=.99), quantile(prueba2_imp2$weight, probs=.99), prueba2_imp2$weight_tr)
#truncate weights at 5%
prueba2_imp2$weight_tr2 <- ifelse(prueba2_imp2$weight < quantile(prueba2_imp2$weight, probs=.05), quantile(prueba2_imp2$weight, probs=.05), prueba2_imp2$weight)
prueba2_imp2$weight_tr2 <- ifelse(prueba2_imp2$weight > quantile(prueba2_imp2$weight, probs=.95), quantile(prueba2_imp2$weight, probs=.95), prueba2_imp2$weight_tr2)
#truncate weights at 10%
prueba2_imp2$weight_tr3 <- ifelse(prueba2_imp2$weight < quantile(prueba2_imp2$weight, probs=.1), quantile(prueba2_imp2$weight, probs=.1), prueba2_imp2$weight)
prueba2_imp2$weight_tr3 <- ifelse(prueba2_imp2$weight > quantile(prueba2_imp2$weight, probs=.9), quantile(prueba2_imp2$weight, probs=.9), prueba2_imp2$weight_tr3)
cat("===============================================================================")
logit2 <- glm ( as.formula(paste("con_quien_vive_joel_alone~", paste(vars, collapse = "+"))),
data=prueba2_imp2, family=binomial(link= "logit"))
pred2 <- predict(logit2, type="response")
prueba2_imp2$weight_a <- prueba2_imp2$con_quien_vive_joel_alone/pred2 + (1 - prueba2_imp2$con_quien_vive_joel_alone)/(1-pred2)
#https://anthonyongphd.files.wordpress.com/2016/02/emerging-adulthood-2016-thoemmes-40-59.pdf
#A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models
#truncate weights at 1%
prueba2_imp2$weight_tr_a <- ifelse(prueba2_imp2$weight_a < quantile(prueba2_imp2$weight_a, probs=.01), quantile(prueba2_imp2$weight_a, probs=.01), prueba2_imp2$weight_a)
prueba2_imp2$weight_tr_a <- ifelse(prueba2_imp2$weight_a > quantile(prueba2_imp2$weight_a, probs=.99), quantile(prueba2_imp2$weight_a, probs=.99), prueba2_imp2$weight_tr_a)
#truncate weights at 5%
prueba2_imp2$weight_tr2_a <- ifelse(prueba2_imp2$weight_a < quantile(prueba2_imp2$weight_a, probs=.05), quantile(prueba2_imp2$weight, probs=.05), prueba2_imp2$weight)
prueba2_imp2$weight_tr2_a <- ifelse(prueba2_imp2$weight_a > quantile(prueba2_imp2$weight_a, probs=.95), quantile(prueba2_imp2$weight_a, probs=.95), prueba2_imp2$weight_tr2_a)
#truncate weights at 5%
prueba2_imp2$weight_tr3_b <- ifelse(prueba2_imp2$weight_a < quantile(prueba2_imp2$weight_a, probs=.1), quantile(prueba2_imp2$weight_a, probs=.1), prueba2_imp2$weight_b)
prueba2_imp2$weight_tr3_b <- ifelse(prueba2_imp2$weight_a > quantile(prueba2_imp2$weight_a, probs=.9), quantile(prueba2_imp2$weight_a, probs=.9), prueba2_imp2$weight_tr3_b)
cat("===============================================================================")
logit3 <- glm (as.formula(paste("con_quien_vive_joel_coup_chil~", paste(vars, collapse = "+"))),
data=prueba2_imp2, family=binomial(link= "logit"))
pred3 <- predict(logit3, type="response")
prueba2_imp2$weight_b <- prueba2_imp2$con_quien_vive_joel_coup_chil/pred + (1 - prueba2_imp2$con_quien_vive_joel_coup_chil)/(1-pred)
#https://anthonyongphd.files.wordpress.com/2016/02/emerging-adulthood-2016-thoemmes-40-59.pdf
#A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models
#truncate weights at 1%
prueba2_imp2$weight_tr_b <- ifelse(prueba2_imp2$weight_b < quantile(prueba2_imp2$weight_b, probs=.01), quantile(prueba2_imp2$weight_b, probs=.01), prueba2_imp2$weight_b)
prueba2_imp2$weight_tr_b <- ifelse(prueba2_imp2$weight_b > quantile(prueba2_imp2$weight_b, probs=.99), quantile(prueba2_imp2$weight_b, probs=.99), prueba2_imp2$weight_tr_b)
#truncate weights at 5%
prueba2_imp2$weight_tr2_b <- ifelse(prueba2_imp2$weight_b < quantile(prueba2_imp2$weight_b, probs=.05), quantile(prueba2_imp2$weight_b, probs=.05), prueba2_imp2$weight_b)
prueba2_imp2$weight_tr2_b <- ifelse(prueba2_imp2$weight_b > quantile(prueba2_imp2$weight_b, probs=.95), quantile(prueba2_imp2$weight_b, probs=.95), prueba2_imp2$weight_tr2_b)
#truncate weights at 5%
prueba2_imp2$weight_tr3_b <- ifelse(prueba2_imp2$weight_b < quantile(prueba2_imp2$weight_b, probs=.1), quantile(prueba2_imp2$weight_b, probs=.1), prueba2_imp2$weight_b)
prueba2_imp2$weight_tr3_b <- ifelse(prueba2_imp2$weight_b > quantile(prueba2_imp2$weight_b, probs=.9), quantile(prueba2_imp2$weight_b, probs=.9), prueba2_imp2$weight_tr3_b)
# AKM RMST adjusted for age
source("https://raw.githubusercontent.com/s-conner/akm-rmst/master/AKM_rmst.R")
cat("===============================================================================")
cat("Living with Family of origin(1) vs. Alone and With couple/children at 6 months (adjusted)")
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_fam_or),weight=prueba2_imp2$weight_tr, tau=1)
cat("Living with Family of origin(1) vs. Alone and With couple/children at 3 years (adjusted)")
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_fam_or),weight=prueba2_imp2$weight_tr, tau=3)
cat("Living with Family of origin(1) vs. Alone and With couple/children at 5 years (adjusted)")
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_fam_or),weight=prueba2_imp2$weight_tr, tau=5)
cat("===============================================================================")
cat("Living Alone(1) vs. with Family of origin and With couple/children at 6 months (adjusted)")
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_alone),weight=prueba2_imp2$weight_tr_a, tau=1)
cat("Living Alone(1) vs. with Family of origin and With couple/children at 3 years (adjusted)")
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_alone),weight=prueba2_imp2$weight_tr_a, tau=3)
cat("Living Alone(1) vs. with Family of origin and With couple/children at 5 years (adjusted)")
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_alone),weight=prueba2_imp2$weight_tr_a, tau=5)
cat("===============================================================================")
cat("Living with couple/children(1) vs. Family of origin & alone at 6 months (adjusted)")
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_coup_chil),weight=prueba2_imp2$weight_tr_b, tau=1)
cat("Living with couple/children(1) vs. Family of origin & alone at 3 years (adjusted)")
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_coup_chil),weight=prueba2_imp2$weight_tr_b, tau=3)
cat("Living with couple/children(1) vs. Family of origin & alone at 5 years (adjusted)")
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_coup_chil),weight=prueba2_imp2$weight_tr_b, tau=5)We assess the covariates’ balance after the weighting
Show code
bal_tab<-
cobalt::bal.tab(event ~ con_quien_vive_joel+ edad_al_ing+ sexo_2 + escolaridad_rec + sus_ini_mod_mvv + freq_cons_sus_prin + via_adm_sus_prin_act + condicion_ocupacional_corr+ compromiso_biopsicosocial + numero_de_hijos_mod_joel + tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod + tipo_centro+ cnt_mod_cie_10_dg_cons_sus_or + sus_principal_mod+ macrozona + cnt_mod_cie_10_or, data = prueba2_imp2,
weights = list("Multinomial Logistic, not stabilized" = "weight_mult_a00", "Multinomial Logistic, stabilized at 1%"="weight_tr", "Multinomial Logistic, stabilized at 5%"="weight_tr2", "Multinomial Logistic, stabilized at 10%"="weight_tr3"),
estimand = "ATE",
which.treat = .all,
un = T,
binary = "std", continuous = "std",
stats = c("mean.diffs", "variance.ratios"))
bal_tab$Balance %>%
dplyr::mutate_if(is.numeric, ~round(.,2)) %>%
knitr::kable("markdown", caption="Balance Measures")| Type | Diff.Un | V.Ratio.Un | Diff.Multinomial Logistic, not stabilized | V.Ratio.Multinomial Logistic, not stabilized | Diff.Multinomial Logistic, stabilized at 1% | V.Ratio.Multinomial Logistic, stabilized at 1% | Diff.Multinomial Logistic, stabilized at 5% | V.Ratio.Multinomial Logistic, stabilized at 5% | Diff.Multinomial Logistic, stabilized at 10% | V.Ratio.Multinomial Logistic, stabilized at 10% | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| con_quien_vive_joel_Alone | Binary | 0.02 | -0.03 | -0.01 | 0.01 | 0.02 | |||||
| con_quien_vive_joel_Family of origin | Binary | -0.10 | -0.04 | -0.05 | -0.06 | -0.06 | |||||
| con_quien_vive_joel_With couple/children | Binary | 0.09 | 0.06 | 0.05 | 0.06 | 0.06 | |||||
| edad_al_ing | Contin. | 0.00 | 0.94 | 0.00 | 0.95 | -0.01 | 0.96 | -0.01 | 0.96 | -0.01 | 0.95 |
| sexo_2_Women | Binary | -0.08 | -0.09 | -0.08 | -0.08 | -0.08 | |||||
| escolaridad_rec_3-Completed primary school or less | Binary | 0.16 | 0.16 | 0.16 | 0.16 | 0.16 | |||||
| escolaridad_rec_2-Completed high school or less | Binary | 0.01 | 0.03 | 0.02 | 0.01 | 0.01 | |||||
| escolaridad_rec_1-More than high school | Binary | -0.19 | -0.21 | -0.20 | -0.20 | -0.20 | |||||
| sus_ini_mod_mvv_Alcohol | Binary | -0.09 | -0.09 | -0.09 | -0.09 | -0.09 | |||||
| sus_ini_mod_mvv_Cocaine hydrochloride | Binary | 0.04 | 0.03 | 0.03 | 0.04 | 0.04 | |||||
| sus_ini_mod_mvv_Marijuana | Binary | 0.05 | 0.06 | 0.05 | 0.05 | 0.05 | |||||
| sus_ini_mod_mvv_Other | Binary | -0.01 | -0.01 | -0.01 | -0.01 | -0.01 | |||||
| sus_ini_mod_mvv_Cocaine paste | Binary | 0.06 | 0.06 | 0.07 | 0.07 | 0.07 | |||||
| freq_cons_sus_prin_Less than 1 day a week | Binary | -0.01 | -0.01 | -0.01 | 0.00 | -0.01 | |||||
| freq_cons_sus_prin_1 day a week or more | Binary | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | |||||
| freq_cons_sus_prin_2 to 3 days a week | Binary | 0.02 | 0.03 | 0.03 | 0.02 | 0.02 | |||||
| freq_cons_sus_prin_4 to 6 days a week | Binary | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | |||||
| freq_cons_sus_prin_Daily | Binary | -0.01 | -0.01 | -0.02 | -0.02 | -0.01 | |||||
| via_adm_sus_prin_act_Smoked or Pulmonary Aspiration | Binary | 0.11 | 0.11 | 0.11 | 0.11 | 0.11 | |||||
| via_adm_sus_prin_act_Intranasal (powder aspiration) | Binary | -0.01 | -0.02 | -0.02 | -0.01 | -0.01 | |||||
| via_adm_sus_prin_act_Injected Intravenously or Intramuscularly | Binary | -0.03 | -0.02 | -0.02 | -0.02 | -0.02 | |||||
| via_adm_sus_prin_act_Oral (drunk or eaten) | Binary | -0.13 | -0.12 | -0.12 | -0.12 | -0.13 | |||||
| via_adm_sus_prin_act_Other | Binary | -0.01 | -0.02 | -0.02 | -0.02 | -0.02 | |||||
| condicion_ocupacional_corr_Employed | Binary | 0.10 | 0.10 | 0.10 | 0.10 | 0.10 | |||||
| condicion_ocupacional_corr_Inactive | Binary | -0.08 | -0.09 | -0.09 | -0.08 | -0.08 | |||||
| condicion_ocupacional_corr_Looking for a job for the first time | Binary | -0.01 | 0.00 | -0.01 | -0.01 | -0.01 | |||||
| condicion_ocupacional_corr_No activity | Binary | -0.06 | -0.03 | -0.04 | -0.05 | -0.06 | |||||
| condicion_ocupacional_corr_Not seeking for work | Binary | -0.02 | -0.01 | -0.01 | -0.02 | -0.02 | |||||
| condicion_ocupacional_corr_Unemployed | Binary | -0.02 | -0.03 | -0.02 | -0.02 | -0.02 | |||||
| compromiso_biopsicosocial_1-Mild | Binary | -0.02 | -0.02 | -0.02 | -0.02 | -0.03 | |||||
| compromiso_biopsicosocial_2-Moderate | Binary | 0.02 | 0.01 | 0.02 | 0.02 | 0.02 | |||||
| compromiso_biopsicosocial_3-Severe | Binary | -0.01 | 0.00 | -0.01 | -0.01 | -0.01 | |||||
| numero_de_hijos_mod_joel_0 | Binary | -0.14 | -0.12 | -0.12 | -0.12 | -0.13 | |||||
| numero_de_hijos_mod_joel_1 | Binary | 0.09 | 0.05 | 0.06 | 0.07 | 0.08 | |||||
| numero_de_hijos_mod_joel_2 | Binary | 0.05 | 0.07 | 0.07 | 0.06 | 0.05 | |||||
| numero_de_hijos_mod_joel_3 | Binary | 0.01 | -0.01 | -0.01 | 0.00 | 0.00 | |||||
| numero_de_hijos_mod_joel_4 or more | Binary | 0.06 | 0.07 | 0.06 | 0.06 | 0.05 | |||||
| tipo_de_plan_2_mod_PAB | Binary | 0.16 | 0.16 | 0.16 | 0.16 | 0.16 | |||||
| tipo_de_plan_2_mod_PAI | Binary | -0.06 | -0.06 | -0.06 | -0.05 | -0.05 | |||||
| tipo_de_plan_2_mod_PR | Binary | -0.13 | -0.13 | -0.14 | -0.15 | -0.15 | |||||
| tenencia_de_la_vivienda_mod_Illegal Settlement | Binary | 0.02 | -0.02 | -0.02 | -0.02 | -0.01 | |||||
| tenencia_de_la_vivienda_mod_Others | Binary | 0.02 | 0.04 | 0.03 | 0.03 | 0.02 | |||||
| tenencia_de_la_vivienda_mod_Owner/Transferred dwellings/Pays Dividends | Binary | -0.06 | -0.03 | -0.03 | -0.04 | -0.04 | |||||
| tenencia_de_la_vivienda_mod_Renting | Binary | 0.01 | -0.02 | -0.02 | -0.02 | -0.02 | |||||
| tenencia_de_la_vivienda_mod_Stays temporarily with a relative | Binary | 0.04 | 0.03 | 0.04 | 0.05 | 0.05 | |||||
| tipo_centro_Public | Binary | 0.10 | 0.10 | 0.11 | 0.11 | 0.11 | |||||
| cnt_mod_cie_10_dg_cons_sus_or | Contin. | -0.17 | 0.89 | -0.16 | 0.88 | -0.17 | 0.88 | -0.17 | 0.89 | -0.17 | 0.89 |
| sus_principal_mod_Alcohol | Binary | -0.12 | -0.11 | -0.12 | -0.12 | -0.12 | |||||
| sus_principal_mod_Cocaine hydrochloride | Binary | -0.01 | -0.02 | -0.02 | -0.01 | -0.01 | |||||
| sus_principal_mod_Marijuana | Binary | -0.10 | -0.09 | -0.09 | -0.10 | -0.10 | |||||
| sus_principal_mod_Other | Binary | -0.07 | -0.05 | -0.05 | -0.06 | -0.06 | |||||
| sus_principal_mod_Cocaine paste | Binary | 0.19 | 0.18 | 0.18 | 0.18 | 0.18 | |||||
| macrozona_Center | Binary | 0.14 | 0.12 | 0.13 | 0.14 | 0.14 | |||||
| macrozona_North | Binary | -0.01 | -0.01 | -0.01 | -0.02 | -0.02 | |||||
| macrozona_South | Binary | -0.19 | -0.17 | -0.18 | -0.18 | -0.18 | |||||
| cnt_mod_cie_10_or_0 | Binary | -0.06 | -0.09 | -0.09 | -0.08 | -0.07 | |||||
| cnt_mod_cie_10_or_1 | Binary | 0.08 | 0.11 | 0.11 | 0.10 | 0.09 | |||||
| cnt_mod_cie_10_or_2 | Binary | -0.06 | -0.08 | -0.08 | -0.07 | -0.07 | |||||
| cnt_mod_cie_10_or_3 | Binary | -0.03 | -0.04 | -0.04 | -0.04 | -0.04 |
Show code
#knitr::kable("html", caption="Balance Measures") %>%
# kableExtra::kable_classic_2()Show code
#The “effective sample size” (ESS) is a measure of the sample size a non-weighted sample would have to have to achieve the same level of precision as the weighted sample (Ridgeway 2006).
bal_tab$Observations %>%
#dplyr::mutate_if(is.numeric, ~round(.,2)) %>%
knitr::kable("markdown", caption="Effective sample sizes")| Control | Treated | |
|---|---|---|
| All | 7768.000 | 16211.000 |
| Multinomial Logistic, not stabilized | 4088.380 | 8946.629 |
| Multinomial Logistic, stabilized at 1% | 4900.288 | 10400.057 |
| Multinomial Logistic, stabilized at 5% | 6330.502 | 13183.043 |
| Multinomial Logistic, stabilized at 10% | 6881.947 | 14286.005 |
Educational attainment in “More than high school” category had great differences between groups. Also the South Macrozone showed high standardized differences, meaning that the sample could not be balanced yet. Stabilizing at 5% and 10% behaves better and obtain greater effective sample sizes, which may yield greater precision of the associations.
Alternative analyses
We tested weigths that account for a multinomial response using Propensity score weighting using Bayesian additive regression trees (BART) (weight_mult_a0), multinomial regression (weight_mult_a), and Generalized Boosted Regression Modeling with multinomial response on the average effect on the treated or ATE (weight_mult_b), weights with multiple treatments or ATO (Li, & Li, 2019) (weight_mult_c) and weights to simultaneously compare three treatment groups or ATM (Yoshida et al., 2007) (weight_mult_d).
Show code
#load(paste0(dirname(rstudioapi::getSourceEditorContext()$path),"/__analisis_joel.RData"))
prueba2_imp22<-prueba2_imp2 %>% dplyr::mutate(con_quien_vive_joel= factor(con_quien_vive_joel))#, numero_de_hijos_mod_joel= as.ordered(numero_de_hijos_mod_joel)) %>% dplyr::mutate(freq_cons_sus_prin=ordered(freq_cons_sus_prin,levels=c("Did not use", "Less than 1 day a week", "1 day a week or more", "2 to 3 days a week","4 to 6 days a week", "Daily")))
#A multi-category example using GBM predicted probabilities
library(caret)Show code
set.seed(2125)
#Here, we'll set multinomial distribution, 10 cross-validation fold, and 200 trees.
#learning rate (shrinkage) of 0.001. This is a very small learning rate and typically requires a large number of trees to find the minimum MSE.
#find the better model
#https://www.webpages.uidaho.edu/~stevel/517/Gradient%20Boosting%20Machines%20at%20UC.html
W1 <- weightit(as.formula(paste("con_quien_vive_joel~", paste(vars, collapse = "+"))),
data=prueba2_imp22, stabilize = T,
method = "bart", estimand = "ATE", int=T,
use.mlogit = FALSE)
prueba2_imp22$weight_mult_a0 <-W1$weights
W2 <- weightit(as.formula(paste("con_quien_vive_joel~", paste(vars, collapse = "+"))),
data=prueba2_imp22, stabilize = T,
method = "ps", estimand = "ATE", int=T,
use.mlogit = FALSE)
prueba2_imp22$weight_mult_a <-W2$weights
mod_gbm = gbm::gbm(as.formula(paste("con_quien_vive_joel~", paste(vars, collapse = "+"))),
data = prueba2_imp22,
distribution = "multinomial",
cv.folds = 10,
shrinkage = .001,
n.minobsinnode = 10,
n.trees = 5e3)
#gbm.perf(mod_gbm, method = "cv") weightit.fit
ps.multi <- drop(predict(mod_gbm, type = "response", n.trees = 5e3))
#https://ngreifer.github.io/WeightIt/reference/method_ps.html
#https://search.r-project.org/CRAN/refmans/WeightIt/html/get_w_from_ps.html
prueba2_imp22$weight_mult_b <- WeightIt::get_w_from_ps(ps.multi, prueba2_imp22$con_quien_vive_joel, estimand = "ATE", stabilize=T)
prueba2_imp22$weight_mult_c <- WeightIt::get_w_from_ps(ps.multi, prueba2_imp22$con_quien_vive_joel, estimand = "ATO", stabilize=T)
prueba2_imp22$weight_mult_d <- WeightIt::get_w_from_ps(ps.multi, prueba2_imp22$con_quien_vive_joel, estimand = "ATM", stabilize=T)
# Multinomial Treatments
#
# - estimand = "ATO"
# Li, F., & Li, F. (2019). Propensity score weighting for causal inference with multiple treatments. The Annals of Applied Statistics, 13(4), 2389<U+2013>2415. 10.1214/19-AOAS1282
#
# - estimand = "ATM"We assess the covariates’ balance after the weighting
Show code
bal_tab2<-
cobalt::bal.tab(event ~ con_quien_vive_joel+ edad_al_ing+ sexo_2 + escolaridad_rec + sus_ini_mod_mvv + freq_cons_sus_prin + via_adm_sus_prin_act + condicion_ocupacional_corr+ compromiso_biopsicosocial + numero_de_hijos_mod_joel + tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod + tipo_centro+ cnt_mod_cie_10_dg_cons_sus_or + sus_principal_mod+ macrozona + cnt_mod_cie_10_or, data = prueba2_imp22,
weights = list("Multinomial Logistic, not stabilized" = "weight_mult_a00", "Multinomial Logistic, stabilized at 1%"="weight_tr", "Multinomial Logistic, stabilized at 5%"="weight_tr2", "Multinomial Logistic, stabilized at 10%"="weight_tr3", "Bayesian additive regression trees (BART)"="weight_mult_a0", "Propensity Scores (WeightIt)"="weight_mult_a", "Generalized Boosted Regression Modeling with multinomial response, ATE"="weight_mult_b", "Generalized Boosted Regression Modeling with multinomial response, ATO"="weight_mult_c", "Generalized Boosted Regression Modeling with multinomial response, ATM"="weight_mult_d"),
estimand = "ATE",
which.treat = .all,
un = T,
thresholds = c(cor = .2),
binary = "std", continuous = "std",
stats = c("mean.diffs", "variance.ratios"))
bal_tab2$Balance %>%
dplyr::mutate_if(is.numeric, ~round(.,2)) %>%
knitr::kable("markdown", caption="Balance Measures (multinomial response weights)")| Type | Diff.Un | V.Ratio.Un | Diff.Multinomial Logistic, not stabilized | V.Ratio.Multinomial Logistic, not stabilized | Diff.Multinomial Logistic, stabilized at 1% | V.Ratio.Multinomial Logistic, stabilized at 1% | Diff.Multinomial Logistic, stabilized at 5% | V.Ratio.Multinomial Logistic, stabilized at 5% | Diff.Multinomial Logistic, stabilized at 10% | V.Ratio.Multinomial Logistic, stabilized at 10% | Diff.Bayesian additive regression trees (BART) | V.Ratio.Bayesian additive regression trees (BART) | Diff.Propensity Scores (WeightIt) | V.Ratio.Propensity Scores (WeightIt) | Diff.Generalized Boosted Regression Modeling with multinomial response, ATE | V.Ratio.Generalized Boosted Regression Modeling with multinomial response, ATE | Diff.Generalized Boosted Regression Modeling with multinomial response, ATO | V.Ratio.Generalized Boosted Regression Modeling with multinomial response, ATO | Diff.Generalized Boosted Regression Modeling with multinomial response, ATM | V.Ratio.Generalized Boosted Regression Modeling with multinomial response, ATM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| con_quien_vive_joel_Alone | Binary | 0.02 | -0.03 | -0.01 | 0.01 | 0.02 | -0.05 | -0.03 | 0.01 | 0.03 | 0.03 | ||||||||||
| con_quien_vive_joel_Family of origin | Binary | -0.10 | -0.04 | -0.05 | -0.06 | -0.06 | -0.04 | -0.04 | -0.07 | -0.09 | -0.11 | ||||||||||
| con_quien_vive_joel_With couple/children | Binary | 0.09 | 0.06 | 0.05 | 0.06 | 0.06 | 0.07 | 0.06 | 0.06 | 0.08 | 0.09 | ||||||||||
| edad_al_ing | Contin. | 0.00 | 0.94 | 0.00 | 0.95 | -0.01 | 0.96 | -0.01 | 0.96 | -0.01 | 0.95 | 0.00 | 0.96 | 0.00 | 0.95 | -0.01 | 0.95 | -0.04 | 0.98 | -0.05 | 0.99 |
| sexo_2_Women | Binary | -0.08 | -0.09 | -0.08 | -0.08 | -0.08 | -0.09 | -0.09 | -0.08 | -0.07 | -0.07 | ||||||||||
| escolaridad_rec_3-Completed primary school or less | Binary | 0.16 | 0.16 | 0.16 | 0.16 | 0.16 | 0.16 | 0.16 | 0.16 | 0.17 | 0.18 | ||||||||||
| escolaridad_rec_2-Completed high school or less | Binary | 0.01 | 0.03 | 0.02 | 0.01 | 0.01 | 0.03 | 0.03 | 0.02 | 0.02 | 0.03 | ||||||||||
| escolaridad_rec_1-More than high school | Binary | -0.19 | -0.21 | -0.20 | -0.20 | -0.20 | -0.21 | -0.21 | -0.21 | -0.22 | -0.23 | ||||||||||
| sus_ini_mod_mvv_Alcohol | Binary | -0.09 | -0.09 | -0.09 | -0.09 | -0.09 | -0.09 | -0.09 | -0.09 | -0.10 | -0.11 | ||||||||||
| sus_ini_mod_mvv_Cocaine hydrochloride | Binary | 0.04 | 0.03 | 0.03 | 0.04 | 0.04 | 0.03 | 0.03 | 0.04 | 0.04 | 0.05 | ||||||||||
| sus_ini_mod_mvv_Marijuana | Binary | 0.05 | 0.06 | 0.05 | 0.05 | 0.05 | 0.06 | 0.06 | 0.05 | 0.05 | 0.06 | ||||||||||
| sus_ini_mod_mvv_Other | Binary | -0.01 | -0.01 | -0.01 | -0.01 | -0.01 | -0.01 | -0.01 | -0.01 | 0.01 | 0.02 | ||||||||||
| sus_ini_mod_mvv_Cocaine paste | Binary | 0.06 | 0.06 | 0.07 | 0.07 | 0.07 | 0.05 | 0.06 | 0.06 | 0.07 | 0.08 | ||||||||||
| freq_cons_sus_prin_Less than 1 day a week | Binary | -0.01 | -0.01 | -0.01 | 0.00 | -0.01 | -0.01 | -0.01 | -0.01 | 0.00 | 0.01 | ||||||||||
| freq_cons_sus_prin_1 day a week or more | Binary | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | -0.02 | ||||||||||
| freq_cons_sus_prin_2 to 3 days a week | Binary | 0.02 | 0.03 | 0.03 | 0.02 | 0.02 | 0.02 | 0.03 | 0.03 | 0.04 | 0.04 | ||||||||||
| freq_cons_sus_prin_4 to 6 days a week | Binary | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | ||||||||||
| freq_cons_sus_prin_Daily | Binary | -0.01 | -0.01 | -0.02 | -0.02 | -0.01 | -0.01 | -0.02 | -0.01 | -0.03 | -0.04 | ||||||||||
| via_adm_sus_prin_act_Smoked or Pulmonary Aspiration | Binary | 0.11 | 0.11 | 0.11 | 0.11 | 0.11 | 0.11 | 0.11 | 0.11 | 0.10 | 0.10 | ||||||||||
| via_adm_sus_prin_act_Intranasal (powder aspiration) | Binary | -0.01 | -0.02 | -0.02 | -0.01 | -0.01 | -0.02 | -0.02 | -0.01 | -0.01 | -0.01 | ||||||||||
| via_adm_sus_prin_act_Injected Intravenously or Intramuscularly | Binary | -0.03 | -0.02 | -0.02 | -0.02 | -0.02 | -0.01 | -0.02 | -0.02 | -0.02 | -0.03 | ||||||||||
| via_adm_sus_prin_act_Oral (drunk or eaten) | Binary | -0.13 | -0.12 | -0.12 | -0.12 | -0.13 | -0.11 | -0.12 | -0.12 | -0.12 | -0.12 | ||||||||||
| via_adm_sus_prin_act_Other | Binary | -0.01 | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 | -0.01 | 0.00 | ||||||||||
| condicion_ocupacional_corr_Employed | Binary | 0.10 | 0.10 | 0.10 | 0.10 | 0.10 | 0.10 | 0.09 | 0.09 | 0.09 | 0.09 | ||||||||||
| condicion_ocupacional_corr_Inactive | Binary | -0.08 | -0.09 | -0.09 | -0.08 | -0.08 | -0.08 | -0.09 | -0.08 | -0.08 | -0.08 | ||||||||||
| condicion_ocupacional_corr_Looking for a job for the first time | Binary | -0.01 | 0.00 | -0.01 | -0.01 | -0.01 | -0.01 | 0.00 | -0.01 | 0.00 | 0.00 | ||||||||||
| condicion_ocupacional_corr_No activity | Binary | -0.06 | -0.03 | -0.04 | -0.05 | -0.06 | -0.02 | -0.03 | -0.04 | -0.04 | -0.04 | ||||||||||
| condicion_ocupacional_corr_Not seeking for work | Binary | -0.02 | -0.01 | -0.01 | -0.02 | -0.02 | -0.01 | -0.01 | -0.02 | -0.02 | -0.03 | ||||||||||
| condicion_ocupacional_corr_Unemployed | Binary | -0.02 | -0.03 | -0.02 | -0.02 | -0.02 | -0.04 | -0.03 | -0.02 | -0.02 | -0.02 | ||||||||||
| compromiso_biopsicosocial_1-Mild | Binary | -0.02 | -0.02 | -0.02 | -0.02 | -0.03 | -0.01 | -0.02 | -0.02 | -0.02 | -0.02 | ||||||||||
| compromiso_biopsicosocial_2-Moderate | Binary | 0.02 | 0.01 | 0.02 | 0.02 | 0.02 | 0.00 | 0.01 | 0.02 | 0.02 | 0.01 | ||||||||||
| compromiso_biopsicosocial_3-Severe | Binary | -0.01 | 0.00 | -0.01 | -0.01 | -0.01 | 0.01 | 0.00 | -0.01 | 0.00 | 0.00 | ||||||||||
| numero_de_hijos_mod_joel_0 | Binary | -0.14 | -0.12 | -0.12 | -0.12 | -0.13 | -0.11 | -0.11 | -0.12 | -0.10 | -0.09 | ||||||||||
| numero_de_hijos_mod_joel_1 | Binary | 0.09 | 0.05 | 0.06 | 0.07 | 0.08 | 0.04 | 0.05 | 0.07 | 0.05 | 0.05 | ||||||||||
| numero_de_hijos_mod_joel_2 | Binary | 0.05 | 0.07 | 0.07 | 0.06 | 0.05 | 0.07 | 0.07 | 0.06 | 0.05 | 0.04 | ||||||||||
| numero_de_hijos_mod_joel_3 | Binary | 0.01 | -0.01 | -0.01 | 0.00 | 0.00 | 0.00 | -0.01 | 0.00 | -0.02 | -0.02 | ||||||||||
| numero_de_hijos_mod_joel_4 or more | Binary | 0.06 | 0.07 | 0.06 | 0.06 | 0.05 | 0.07 | 0.07 | 0.06 | 0.07 | 0.07 | ||||||||||
| tipo_de_plan_2_mod_PAB | Binary | 0.16 | 0.16 | 0.16 | 0.16 | 0.16 | 0.14 | 0.16 | 0.16 | 0.18 | 0.19 | ||||||||||
| tipo_de_plan_2_mod_PAI | Binary | -0.06 | -0.06 | -0.06 | -0.05 | -0.05 | -0.05 | -0.06 | -0.06 | -0.04 | -0.05 | ||||||||||
| tipo_de_plan_2_mod_PR | Binary | -0.13 | -0.13 | -0.14 | -0.15 | -0.15 | -0.12 | -0.13 | -0.14 | -0.19 | -0.20 | ||||||||||
| tenencia_de_la_vivienda_mod_Illegal Settlement | Binary | 0.02 | -0.02 | -0.02 | -0.02 | -0.01 | -0.02 | -0.02 | -0.02 | -0.04 | -0.03 | ||||||||||
| tenencia_de_la_vivienda_mod_Others | Binary | 0.02 | 0.04 | 0.03 | 0.03 | 0.02 | 0.04 | 0.04 | 0.03 | 0.06 | 0.07 | ||||||||||
| tenencia_de_la_vivienda_mod_Owner/Transferred dwellings/Pays Dividends | Binary | -0.06 | -0.03 | -0.03 | -0.04 | -0.04 | -0.02 | -0.03 | -0.04 | -0.01 | -0.01 | ||||||||||
| tenencia_de_la_vivienda_mod_Renting | Binary | 0.01 | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 | -0.01 | -0.02 | -0.05 | -0.05 | ||||||||||
| tenencia_de_la_vivienda_mod_Stays temporarily with a relative | Binary | 0.04 | 0.03 | 0.04 | 0.05 | 0.05 | 0.03 | 0.03 | 0.05 | 0.04 | 0.03 | ||||||||||
| tipo_centro_Public | Binary | 0.10 | 0.10 | 0.11 | 0.11 | 0.11 | 0.10 | 0.10 | 0.11 | 0.12 | 0.12 | ||||||||||
| cnt_mod_cie_10_dg_cons_sus_or | Contin. | -0.17 | 0.89 | -0.16 | 0.88 | -0.17 | 0.88 | -0.17 | 0.89 | -0.17 | 0.89 | -0.16 | 0.88 | -0.16 | 0.88 | -0.16 | 0.89 | -0.16 | 0.87 | -0.16 | 0.86 |
| sus_principal_mod_Alcohol | Binary | -0.12 | -0.11 | -0.12 | -0.12 | -0.12 | -0.11 | -0.11 | -0.12 | -0.11 | -0.11 | ||||||||||
| sus_principal_mod_Cocaine hydrochloride | Binary | -0.01 | -0.02 | -0.02 | -0.01 | -0.01 | -0.02 | -0.02 | -0.01 | -0.01 | -0.01 | ||||||||||
| sus_principal_mod_Marijuana | Binary | -0.10 | -0.09 | -0.09 | -0.10 | -0.10 | -0.09 | -0.09 | -0.10 | -0.10 | -0.11 | ||||||||||
| sus_principal_mod_Other | Binary | -0.07 | -0.05 | -0.05 | -0.06 | -0.06 | -0.04 | -0.05 | -0.06 | -0.05 | -0.04 | ||||||||||
| sus_principal_mod_Cocaine paste | Binary | 0.19 | 0.18 | 0.18 | 0.18 | 0.18 | 0.17 | 0.18 | 0.18 | 0.17 | 0.18 | ||||||||||
| macrozona_Center | Binary | 0.14 | 0.12 | 0.13 | 0.14 | 0.14 | 0.11 | 0.12 | 0.14 | 0.14 | 0.14 | ||||||||||
| macrozona_North | Binary | -0.01 | -0.01 | -0.01 | -0.02 | -0.02 | 0.00 | -0.01 | -0.02 | -0.02 | -0.02 | ||||||||||
| macrozona_South | Binary | -0.19 | -0.17 | -0.18 | -0.18 | -0.18 | -0.17 | -0.17 | -0.18 | -0.18 | -0.18 | ||||||||||
| cnt_mod_cie_10_or_0 | Binary | -0.06 | -0.09 | -0.09 | -0.08 | -0.07 | -0.09 | -0.09 | -0.08 | -0.09 | -0.09 | ||||||||||
| cnt_mod_cie_10_or_1 | Binary | 0.08 | 0.11 | 0.11 | 0.10 | 0.09 | 0.11 | 0.12 | 0.11 | 0.11 | 0.11 | ||||||||||
| cnt_mod_cie_10_or_2 | Binary | -0.06 | -0.08 | -0.08 | -0.07 | -0.07 | -0.08 | -0.08 | -0.07 | -0.06 | -0.07 | ||||||||||
| cnt_mod_cie_10_or_3 | Binary | -0.03 | -0.04 | -0.04 | -0.04 | -0.04 | -0.04 | -0.04 | -0.04 | -0.04 | -0.04 |
Show code
# knitr::kable("html", caption="Balance Measures") %>%
# kableExtra::kable_classic()Show code
| Control | Treated | |
|---|---|---|
| All | 7768.000 | 16211.000 |
| Multinomial Logistic, not stabilized | 4088.380 | 8946.629 |
| Multinomial Logistic, stabilized at 1% | 4900.288 | 10400.057 |
| Multinomial Logistic, stabilized at 5% | 6330.502 | 13183.043 |
| Multinomial Logistic, stabilized at 10% | 6881.947 | 14286.005 |
| Bayesian additive regression trees (BART) | 3460.427 | 7771.637 |
| Propensity Scores (WeightIt) | 4056.515 | 8753.682 |
| Generalized Boosted Regression Modeling with multinomial response, ATE | 5527.201 | 11488.753 |
| Generalized Boosted Regression Modeling with multinomial response, ATO | 3426.545 | 7361.840 |
| Generalized Boosted Regression Modeling with multinomial response, ATM | 2430.292 | 5186.401 |
The model with multinomial logistic regression (weight_mult_a) performed better in terms of balance of the variables. We estimate the restricted survival times using these weights.
Show code
#(3) (PDF) Adjusted restricted mean survival times in observational studies. Available from: https://www.researchgate.net/publication/333326572_Adjusted_restricted_mean_survival_times_in_observational_studies [accessed Feb 02 2023].
akm_rmst(time=prueba2_imp22$yrs_to_tr_dropout, status=prueba2_imp22$event, group=prueba2_imp22$con_quien_vive_joel,weight=prueba2_imp22$weight_mult_a, tau=.5)
RMST calculated up to tau = 0.5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 0.383 0.008
Group Family of origin 0.385 0.001
Group With couple/children 0.377 0.003
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone 0.002 0.008
Groups With couple/children vs. Alone -0.006 0.009
Groups With couple/children vs. Family of origin -0.008 0.003
CIL CIU
Groups Family of origin vs. Alone -0.014 0.018
Groups With couple/children vs. Alone -0.022 0.011
Groups With couple/children vs. Family of origin -0.014 -0.002
p
Groups Family of origin vs. Alone 0.818
Groups With couple/children vs. Alone 0.502
Groups With couple/children vs. Family of origin 0.013
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone 0.005 0.021
Groups With couple/children vs. Alone -0.015 0.022
Groups With couple/children vs. Family of origin -0.020 0.008
Est. CIL CIU
Groups Family of origin vs. Alone 1.005 0.964 1.048
Groups With couple/children vs. Alone 0.985 0.943 1.029
Groups With couple/children vs. Family of origin 0.980 0.965 0.996
p
Groups Family of origin vs. Alone 0.819
Groups With couple/children vs. Alone 0.500
Groups With couple/children vs. Family of origin 0.013Show code
akm_rmst(time=prueba2_imp22$yrs_to_tr_dropout, status=prueba2_imp22$event, group=prueba2_imp22$con_quien_vive_joel,weight=prueba2_imp22$weight_mult_a, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 0.594 0.020
Group Family of origin 0.593 0.003
Group With couple/children 0.572 0.006
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone -0.001 0.020
Groups With couple/children vs. Alone -0.022 0.021
Groups With couple/children vs. Family of origin -0.021 0.007
CIL CIU
Groups Family of origin vs. Alone -0.040 0.039
Groups With couple/children vs. Alone -0.062 0.019
Groups With couple/children vs. Family of origin -0.035 -0.007
p
Groups Family of origin vs. Alone 0.973
Groups With couple/children vs. Alone 0.300
Groups With couple/children vs. Family of origin 0.004
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone -0.001 0.034
Groups With couple/children vs. Alone -0.037 0.035
Groups With couple/children vs. Family of origin -0.036 0.013
Est. CIL CIU
Groups Family of origin vs. Alone 0.999 0.935 1.067
Groups With couple/children vs. Alone 0.964 0.900 1.032
Groups With couple/children vs. Family of origin 0.965 0.941 0.989
p
Groups Family of origin vs. Alone 0.973
Groups With couple/children vs. Alone 0.293
Groups With couple/children vs. Family of origin 0.004Show code
akm_rmst(time=prueba2_imp22$yrs_to_tr_dropout, status=prueba2_imp22$event, group=prueba2_imp22$con_quien_vive_joel,weight=prueba2_imp22$weight_mult_a, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group Alone 1.247 0.072
Group Family of origin 1.219 0.012
Group With couple/children 1.143 0.022
Restricted Mean Survival Time (RMST) Differences
Est. SE
Groups Family of origin vs. Alone -0.027 0.073
Groups With couple/children vs. Alone -0.104 0.075
Groups With couple/children vs. Family of origin -0.076 0.025
CIL CIU
Groups Family of origin vs. Alone -0.170 0.115
Groups With couple/children vs. Alone -0.251 0.044
Groups With couple/children vs. Family of origin -0.125 -0.027
p
Groups Family of origin vs. Alone 0.706
Groups With couple/children vs. Alone 0.167
Groups With couple/children vs. Family of origin 0.002
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE
Groups Family of origin vs. Alone -0.022 0.058
Groups With couple/children vs. Alone -0.087 0.061
Groups With couple/children vs. Family of origin -0.065 0.022
Est. CIL CIU
Groups Family of origin vs. Alone 0.978 0.872 1.097
Groups With couple/children vs. Alone 0.917 0.814 1.033
Groups With couple/children vs. Family of origin 0.937 0.899 0.978
p
Groups Family of origin vs. Alone 0.703
Groups With couple/children vs. Alone 0.153
Groups With couple/children vs. Family of origin 0.003- Who lives with his/her couple and children have lower mean time without experiencing dropout in comparison with vs. Family of origin and alone, meaning that experience it more quickly.
Show code
#Asymptotic variances are calculated using the equations given in Xie and Liu (2005). It is also recommended to use stabilized weights by using stabilize=TRUE (the default value). More information can be found in the cited literature.
invisible(" Error in `stop_subscript()`:
# ! Can't subset columns that don't exist.
# x Column `vars` doesn't exist.")
logit2_adj_curv <- nnet::multinom(formula= con_quien_vive_joel~ edad_al_ing+sexo_2+escolaridad_rec+sus_ini_mod_mvv+freq_cons_sus_prin+via_adm_sus_prin_act+condicion_ocupacional_corr+compromiso_biopsicosocial+numero_de_hijos_mod_joel+tipo_de_plan_2_mod+tenencia_de_la_vivienda_mod+tipo_centro+cnt_mod_cie_10_dg_cons_sus_or+sus_principal_mod+macrozona+cnt_mod_cie_10_or, data=prueba2_imp2)
cat("========================================================")
#https://github.com/RobinDenz1/adjustedCurves
#https://cran.rstudio.com/web/packages/adjustedCurves/vignettes/introduction.html
# Those weights are used in a weighted version of the Kaplan-Meier estimator proposed by Xie and Liu (2005)
adjsurv <- adjustedCurves::adjustedsurv(data=prueba2_imp22,
variable="con_quien_vive_joel",
ev_time="yrs_to_tr_dropout",
event="event",
method="iptw_km",
treatment_model=logit2_adj_curv, stabilize=T,
bootstrap=TRUE, n_boot=500)
adjustedCurves::plot_curve_diff(adjsurv, group_1= "Family of origin", group_2="Alone", type="points")
adjustedCurves::plot_curve_diff(adjsurv, group_1= "Family of origin", group_2="With couple/children", type="points")
adjustedCurves::adjusted_curve_test(adjsurv, from=.5, to=.51)
adjustedCurves::adjusted_curve_test(adjsurv, from=1, to=1.01)
adjustedCurves::adjusted_curve_test(adjsurv, from=2.90, to=2.99)
cat("========================================================")
#https://cran.r-project.org/web/packages/cmprskcoxmsm/cmprskcoxmsm.pdf
adjcif <- cmprskcoxmsm::adjustedcif(data=prueba2_imp22,
variable="con_quien_vive_joel",
ev_time="yrs_to_tr_dropout_cmprsk",
event="event_cmprsk",
method="iptw",
treatment_model=logit2_adj_curv,
cause=1,
ties='efron' ,
conf_int=TRUE)
cat("========================================================")
adjusted_rmtl(adjcif, to=0.5, conf_int=T)
prueba2_imp22$Trt<-prueba2_imp22$con_quien_vive_joel
prueba2_imp22$ipw_ate_stab <- prueba2_imp22$weight_mult_a
cif_est(data=prueba2_imp22,
"yrs_to_tr_dropout_cmprsk", time2 = NULL,
Event.var="event_cmprsk", Events=c(1,2), cif.event=1,
weight.type="Stabilized",
ties='efron',
risktab = TRUE, risk.time = NULL)
invisible("no se obtienen intervalos de confianza de hrcomprisk y toma como 8 hrs con 20 rep")
cat("========================================================")
#Exposure does not have 2 levels
#https://cran.r-project.org/web/packages/hrcomprisk/hrcomprisk.pdf
#https://www.rdocumentation.org/packages/hrcomprisk/versions/0.1.1/topics/hrcomprisk
#https://cran.r-project.org/web/packages/hrcomprisk/vignettes/hrcomprisk.html
prueba<-hrcomprisk::npcrest(df=prueba2_imp22, exit=yrs_to_tr_dropout_cmprsk, ipwvars= vars, event=event_cmprsk, exposure=con_quien_vive_joel_fam_or, rep=2e1, maxtime=3, print.attr=T)
#CIoinc_comp= Value of the unexposed (denoted by “o”) composite cumulative incidence at the given time.
#CIxinc_comp= Value of the exposed (denoted by “x”) composite cumulative incidence at the given time.
#CIoinc_1= Value of the unexposed cumulative incidence of event 1 at the given time.
#CIxinc_1= Value of the exposed cumulative incidence of event 1 at the given time.
# R_1= Sub-hazard ratio/Cause-specific hazard ratio for event 1.
# R_2= Sub-hazard ratio/Cause-specific hazard ratio for event 2.
invisible("no se obtienen intervalos de confianza de hrcomprisk y toma como 8 hrs con 20 rep")
#con_quien_vive_joel_fam_or con_quien_vive_joel_coup_chil con_quien_vive_joel_alone
#
# bootCRCumInc(
# df= prueba2_imp22,
# exit=yrs_to_tr_dropout_cmprsk,
# event=event_cmprsk,
# exposure=con_quien_vive_joel_fam_or,
# entry = NULL,
# weights = ipw_ate_stab,
# ipwvars = NULL,
# rep = 0,
# print.attr = T,
# seed = 54321
# )
cat("========================================================")
#https://github.com/Lesly1031/AdjKM.CIF/
#https://lesly1031.github.io/AdjKM.CIF/
library(AdjKM.CIF)
prueba2_imp22$Trt_num<-as.numeric(prueba2_imp22$con_quien_vive_joel)
sus_principal_mod
cnt_mod_cie_10_or
sus_ini_mod
result1_boot <- AdjKM.CIF::boot_ci_adj_cif(boot_n = 2, ci_cut = c(0.025, 0.975), data = data.matrix(prueba2_imp22), time = "yrs_to_tr_dropout_cmprsk", status = "event_cmprsk", group = "Trt_num", covlist = vars, event_code = 1, stratified= "No", reference_group =NULL)
# Error in solve.default(h, z[[2]]) :
# system is computationally singular: reciprocal condition number = 5.60127e-19
#no aguanta predictores categóricos
#si uso data.matrix, se pitea
# Error in .cif_prob(data = data, coxout = coxout, base_res = CIF0, event_code = event_code, :
# dims [product 674] do not match the length of object [675]
# In addition: Warning message:
# In adjcif + cif :
# longer object length is not a multiple of shorter object lengthWe used the kaplan-meier curves with survey-weighting, according to a commentary made by Thomas Lumley.
Show code
#Rader, Kevin Andrew. 2014. Methods for Analyzing Survival and Binary Data in Complex Surveys. Doctoral dissertation, Harvard University.http://nrs.harvard.edu/urn-3:HUL.InstRepos:12274283
#load(paste0(dirname(rstudioapi::getSourceEditorContext()$path),"/__analisis_joel.RData"))
library(survey)
# survey::svylogrank
#First, you can use the survey::svylogrank function, as @IRTFM suggests. This will treat the weights as sampling weights, but I think that's ok with the robust standard errors that svylogrank uses.
design_weights <- survey::svydesign(ids = ~1, data = dplyr::mutate(prueba2_imp22, yrs_to_tr_dropout= dplyr::case_when(yrs_to_tr_dropout==0~0.0001,T~yrs_to_tr_dropout)), weights = ~weight_mult_a)
# Error in .logrank(formula, design, rho, gamma, ...) :
survey::svykm(Surv(time=yrs_to_tr_dropout,event=event)~con_quien_vive_joel,
#con_quien_vive_joel,
design=design_weights, rho=0, gamma=0)Weighted survival curves:
svykm(formula = Surv(time = yrs_to_tr_dropout, event = event) ~
con_quien_vive_joel, design = design_weights, rho = 0, gamma = 0)
Alone : Q1 = 0.252 median = 0.553 Q3 = Inf
Family of origin : Q1 = 0.266 median = 0.526 Q3 = Inf
With couple/children : Q1 = 0.249 median = 0.498 Q3 = Inf Show code
system.time({
s1<- survey::svylogrank(Surv(time=yrs_to_tr_dropout,event=event)~con_quien_vive_joel_alone,
#con_quien_vive_joel,
design=design_weights, rho=0, gamma=0) #tslow , se=T
#primero es familia de origen vs. el resto
#alone vs. el resto da -41.77638 60.81932 -0.6868932 0.49215
#couple/children vs. el resto da .003
}) user system elapsed
2.38 0.37 2.85 Show code
system.time({
s2<- survey::svylogrank(Surv(time=yrs_to_tr_dropout,event=event)~con_quien_vive_joel_coup_chil,
#con_quien_vive_joel,
design=design_weights, rho=0, gamma=0) #tslow , se=T
#primero es familia de origen vs. el resto
#alone vs. el resto da -41.77638 60.81932 -0.6868932 0.49215
#couple/children vs. el resto da .003
}) user system elapsed
2.36 0.37 4.05 Show code
system.time({
s3<- survey::svylogrank(Surv(time=yrs_to_tr_dropout,event=event)~con_quien_vive_joel_fam_or,
#con_quien_vive_joel,
design=design_weights, rho=0, gamma=0) #tslow , se=T
#primero es familia de origen vs. el resto
#alone vs. el resto da -41.77638 60.81932 -0.6868932 0.49215
#couple/children vs. el resto da .003
}) user system elapsed
2.36 0.28 2.64 Show code
system.time({
s4<- survey::svylogrank(Surv(time=yrs_to_tr_dropout,event=event)~con_quien_vive_joel,
#con_quien_vive_joel,
design=design_weights, rho=0, gamma=0) #tslow , se=T
#primero es familia de origen vs. el resto
#alone vs. el resto da -41.77638 60.81932 -0.6868932 0.49215
#couple/children vs. el resto da .003
}) user system elapsed
3.67 0.30 3.97 Show code
cat("Alone (1) vs. With couple/children and Family of origin")Alone (1) vs. With couple/children and Family of originShow code
s1[[1]]
score
[1,] -41.77638 60.81932 -0.6868932 0.49215
[[2]]
Chisq p
0.4718223 0.4921500
attr(,"class")
[1] "svylogrank"Show code
cat("With couple/children(1) vs. Alone and Family of origin")With couple/children(1) vs. Alone and Family of originShow code
s2[[1]]
score
[1,] 259.0688 87.64619 2.955848 0.003118104
[[2]]
Chisq p
8.737038995 0.003118104
attr(,"class")
[1] "svylogrank"Show code
cat("Family of origin(1) vs. Alone and With couple/children")Family of origin(1) vs. Alone and With couple/childrenShow code
s3[[1]]
score
[1,] -217.2924 93.16219 -2.33241 0.01967912
[[2]]
Chisq p
5.44013793 0.01967912
attr(,"class")
[1] "svylogrank"Show code
cat("Every level")Every levelShow code
s4[[1]]
score se z p
[1,] -217.2924 93.16219 -2.332410 0.019679115
[2,] 259.0688 87.64619 2.955848 0.003118104
[[2]]
Chisq p
8.74116665 0.01264386
attr(,"class")
[1] "svylogrank"Show code
#svykm -->
# Error: cannot allocate vector of size 3.1 Gb
# Timing stopped at: 6913 368.1 7453Show code
plot(survey::svykm(Surv(time=yrs_to_tr_dropout,event=event)~con_quien_vive_joel,
#con_quien_vive_joel,
design=design_weights, rho=0, gamma=0), lwd=2, pars=list(lty=c(1,2,3),col=c("purple","forestgreen", "red")))
legend(8, 1, legend=c("Alone", "Family of origin", "With couple/children"),
fill = c("purple","forestgreen", "red"), lty=c(1,2,3))
Figure 7: Kaplan meier plots (survey weighted)
Export
Show code
vector_var_names<-c("hash_key", "edad_al_ing", "fech_ing", "fech_egres_imp", "event", "diff_bet_treat", "tipo_de_programa_2", "abandono_temprano", "motivodeegreso_mod_imp")
prueba2_imp2%>%
dplyr::arrange(hash_key, edad_al_ing)%>%
#dplyr::left_join(prueba2[,vector_var_names], by=c("hash_key"="hash_key", "edad_al_ing"="edad_al_ing")) %>%
#dplyr::left_join(prueba2_imp2[,c("hash_key","edad_al_ing","duplicates_filtered_adj","person_years","max_cum_dias_trat_sin_na_adj")], by=c("hash_key"="hash_key", "edad_al_ing"="edad_al_ing")) %>%
rio::export(file = paste0(dirname(rstudioapi::getSourceEditorContext()$path),"/CONS_C1_df_dup_SEP_2020_joel_feb_2023.dta"))
if(no_mostrar==1){
#23,979
missing.values2<-
prueba2 %>%
rowwise %>%
dplyr::mutate_all(~ifelse(is.na(.), 1, 0)) %>%
dplyr::ungroup() %>%
dplyr::summarise_all(~sum(.))
invisible("Para ver los nombres de las variables")
for(i in 1:length(prueba)){
print(paste0(names(prueba)[[i]],"= ",attr(prueba[[i]],"label")))
}
} Flowchart
Show code
tab1_lab<- paste0('Original C1 Dataset \n(n = ', formatC(nrow(CONS_C1), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1%>% dplyr::distinct(HASH_KEY)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab2_lab<- paste0('C1 Dataset \n(n = ', formatC(nrow(CONS_C1_df_dup_SEP_2020), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1_df_dup_SEP_2020%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab1_5_lab_1<- paste0('(n=', CONS_C1_df_dup_SEP_2020 %>% dplyr::group_by(hash_key) %>% dplyr::mutate(menor_edad=dplyr::case_when(edad_al_ing<18~1,TRUE~0),menor_edad=sum(menor_edad,na.rm=T)) %>% dplyr::ungroup() %>% dplyr::filter(edad_al_ing<18) %>% nrow(), '; users= ',CONS_C1_df_dup_SEP_2020 %>% dplyr::group_by(hash_key) %>% dplyr::mutate(menor_edad=dplyr::case_when(edad_al_ing<18~1,TRUE~0),menor_edad=sum(menor_edad,na.rm=T)) %>% dplyr::ungroup() %>% dplyr::filter(edad_al_ing<18) %>% dplyr::distinct(hash_key)%>% nrow(), ')')
tab1_5_lab_2<- paste0('(n=', prueba%>% dplyr::filter(edad_al_ing_grupos!="18-29"|is.na(edad_al_ing_grupos)) %>% dplyr::distinct(hash_key) %>% nrow() %>% format(big.mark=","),'; users=', prueba%>% dplyr::filter(edad_al_ing_grupos!="18-29"|is.na(edad_al_ing_grupos)) %>% nrow() %>% format(big.mark=","),')')
tab1_5_lab<- paste0('• Discarded cases with no age at admission or ages below 18 years old ', tab1_5_lab_1,'\\\\\\l• Discarded ages different than between 18-29 years old at admission ',tab1_5_lab_2,'\\\\\\l')
tab4_lab<- paste0('C1 Dataset on Young Adults \n(n = ',format(nrow(prueba2), big.mark=","),')')#, formatC(nrow(CONS_C1_df_dup_SEP_2020_match_miss_after_imp_conservados), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1_df_dup_SEP_2020_match_miss_after_imp_conservados%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab3_5_lab<- paste0('C1 Dataset \n(n = ')#, formatC(nrow(CONS_C1_df_dup_SEP_2020_match_miss_after_imp_descartados), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1_df_dup_SEP_2020_match_miss_after_imp_descartados%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab5_lab<- paste0('• Imputed values of users with at least 1 missing value (n= ',prueba2 %>%
rowwise %>% dplyr::mutate_at(.vars = vars(vector_variables),.funs = ~ifelse(is.na(.), 1, 0)) %>%
dplyr::ungroup() %>% dplyr::select(any_of(vector_variables)) %>% dplyr::mutate(total_na=rowSums(., na.rm=T)) %>% dplyr::filter(total_na>0) %>% nrow() %>% format(big.mark=","),'; ', round(prueba2 %>%
rowwise %>% dplyr::mutate_at(.vars = vars(vector_variables),.funs = ~ifelse(is.na(.), 1, 0)) %>%
dplyr::ungroup() %>% dplyr::select(any_of(vector_variables)) %>% dplyr::mutate(total_na=rowSums(., na.rm=T)) %>% dplyr::filter(total_na>0) %>% nrow()/nrow(prueba2),2)*100 ,'%)\\\\\\l• Substance use onset age and Initial substance were the variables with more missing values and had ', round(parse_number(data.frame(miss_val_bar)[1,3]),0),'% each one \\\\\\l')
lab_tab<- paste0(" Result of the\nimputed dataset\n(n= ",format(nrow(prueba2_imp2),big.mark=","),")")#,table(table(t_id_1)) %>% formatC(big.mark = ","),"; No. of controls: ",table(table(c_id_1))%>% formatC(big.mark = ","))
#https://stackoverflow.com/questions/46750364/diagrammer-and-graphviz
#https://mikeyharper.uk/flowcharts-in-r-using-diagrammer/
#http://blog.nguyenvq.com/blog/2012/05/29/better-decision-tree-graphics-for-rpart-via-party-and-partykit/
#http://blog.nguyenvq.com/blog/2014/01/17/skeleton-to-create-fast-automatic-tree-diagrams-using-r-and-graphviz/
#https://cran.r-project.org/web/packages/DiagrammeR/vignettes/graphviz-mermaid.html
#https://stackoverflow.com/questions/39133058/how-to-use-graphviz-graphs-in-diagrammer-for-r
#https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781789802566/1/ch01lvl1sec21/creating-diagrams-via-the-diagrammer-package
#https://justlegal.be/2019/05/using-flowcharts-to-display-legal-procedures/
# paste0("No. of treatments: ",table(table(t_id_1)) %>% formatC(big.mark = ","),"; No. of controls: ",table(table(c_id_1))%>% formatC(big.mark = ","))
#
library(DiagrammeR) #⋉
flowchrt_p1<-
grViz("digraph flowchart {
# node definitions with substituted label text
node [fontname = Times, shape = rectangle,fontsize = 9]
tab1 [label = '@@1']
tab2 [label = '@@2']
tab3 [label = '•Duplicated entries\\l•Intermediate events of treatment (continuous referrals)\\l',fontsize = 7]
tab4 [label = '@@4']
blank [label = '', width = 0.0001, height = 0.0001]
blank2 [label = '', width = 0.0001, height = 0.0001]
blank3 [label = '', width = 0.0001, height = 0.0001]
tab5 [label = '@@3',fontsize = 7]
tab6 [label= '@@7']
tab7 [label = '@@5',fontsize = 7]
# edge definitions with the node IDs
tab1 -> blank [arrowhead = none,label=' Data wrangling and normalization process',fontsize = 8];
blank -> tab3
blank -> tab2
tab2 -> blank2 [arrowhead = none];
blank2 -> tab5
blank2 -> tab4 [label=' Result of the exclussion criteria',fontsize = 8];
tab4 -> blank3 [arrowhead= none];
blank3-> tab7
blank3 -> tab6 [label='',fontsize = 8];
subgraph {
rank = same; tab3; blank;
}
subgraph {
rank = same; tab5; blank2;
}
subgraph {
rank = same; tab7; blank3;
}
}
[1]: tab1_lab
[2]: tab2_lab
[3]: tab1_5_lab
[4]: tab4_lab
[5]: tab5_lab
[6]: ''
[7]: lab_tab
", width = 1200,
height = 900)
DPI = 1200
WidthCM = 11
HeightCM = 8
flowchrt_p1Show code
flowchrt_p1 %>%
export_svg %>% charToRaw %>% rsvg_pdf("_flowchart_joel.pdf")
flowchrt_p1 %>% export_svg()%>%charToRaw %>% rsvg(width = WidthCM *(DPI/2.54), height = HeightCM *(DPI/2.54)) %>% png::writePNG("_flowchart_joel.png")
htmlwidgets::saveWidget(flowchrt_p1, "_flowchart_joel.html")
webshot::webshot("_flowchart_joel.html", "_flowchart_joel_corr.png",vwidth = 1200, vheight = 900, zoom = 3)
Session Info
##February 15, 2023
Show code
Sys.getenv("R_LIBS_USER")[1] "E:/Mi unidad/Alvacast/SISTRAT 2022 (github)/renv/library/R-4.1/x86_64-w64-mingw32;C:/Users/andre/AppData/Local/Temp/RtmpCkwL6c/renv-system-library"Show code
rstudioapi::getSourceEditorContext()Document Context:
- id: '0D2C335B'
- path: 'E:/Mi unidad/Alvacast/SISTRAT 2022 (github)/analisis_joel2.Rmd'
- contents: <2825 rows>
Document Selection:
- [585, 1] -- [585, 1]: ''Show code
save.image("__analisis_joel.RData")
unlink("*_cache", recursive = T, force = T, expand = TRUE)
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Paquetes estadísticos utilizados')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({'font-size': '80%'});",
"}")))